Innodatainc

<div>Innodata (Nasdaq: INOD) is a global data engineering company. We believe that data and Artificial Intelligence (AI) are inextricably linked. Our mission is to enable the responsible advancement of artificial intelligence by providing the data, evaluation frameworks, and human expertise required to build AI systems that can be trusted at scale. We provide a range of transferable solutions, platforms, and services for Generative AI / AI builders and adopters. In every relationship, we honor our 36+ year legacy delivering the highest quality data and outstanding outcomes for our customers.</div>About the Role:
We build an advanced data platform that helps demanding enterprise and public-sector customers turn large, complex datasets into high-quality, structured data for AI. We're a small, senior team that values ownership, correctness, and strong engineering. We're hiring a full-stack engineer to help build and operate the product end to end, and to work closely with the customers who use it. You'll spend most of your time building, with regular chances to join customer conversations, help deploy the platform in their environments, and see how it's used in the field. It's a great fit for an engineer who enjoys working with customers and wants to grow into a more customer-facing role.
What You'll Do:
<ul>
<li>Build features end to end across our Rust backend and TypeScript/React frontend, from the data model and APIs to a polished, interactive web application.</li>
<li>Design data models, APIs, and ingestion pipelines that are fast and reliable at scale.</li>
<li>Help deploy, configure, and support the platform in cloud and customer environments, including AWS GovCloud, on-premises Kubernetes, and air-gapped or classified networks.</li>
<li>Build data connectors and integrations that bring customer data into the platform and fit it to their workflows.</li>
<li>Join customer conversations, demos, and occasional on-site visits to understand how the product is used, and turn that feedback into product improvements.</li>
<li>Contribute to architecture, code quality, performance, and security as the product grows.</li>
</ul>
What We're Looking For:
<ul>
<li>5+ years building and shipping production software.</li>
<li>Active Top Secret clearance with SCI eligibility (U.S. citizenship required).</li>
<li>Strong backend skills in a modern systems or application language (we work primarily in Rust; we're happy to help a strong engineer ramp up).</li>
<li>Solid SQL and PostgreSQL fundamentals — indexing, transactions, and query performance.</li>
<li>Proficiency with TypeScript and React and an eye for clean, responsive UIs.</li>
<li>Experience deploying and running services with Docker and Kubernetes.</li>
<li>Strong communication skills — able to explain technical work clearly to customers and non-technical stakeholders.</li>
<li>Ability to travel to customer sites.</li>
</ul>
Nice to Have:
<ul>
<li>Customer-facing, consulting, or solutions-engineering experience.</li>
<li>Experience deploying software in classified, air-gapped, or other regulated environments.</li>
<li>AWS/EKS (including GovCloud), infrastructure-as-code, and STIG or RMF experience.</li>
<li>Experience with data-intensive applications, data pipelines, or Python for data work.</li>
<li>Familiarity with AI training data, annotation, or data-labeling workflows.</li>
</ul>
Core Technologies:
<ul>
<li>Rust · TypeScript / React · PostgreSQL · Python · cloud object storage · Docker · Kubernetes · AWS (including GovCloud). (Additional components shared during the interview process.)</li>
</ul>
Eligibility:
<ul>
<li>U.S. citizenship and an active Top Secret clearance with SCI eligibility are required. This role is open to candidates anywhere in the U.S. (remote) or hybrid in the Washington, DC area, with occasional travel to customer sites.</li>
</ul><div>
Please be aware of recruitment scams involving individuals or organizations falsely claiming to represent employers. Innodata will never ask for payment, banking details, or sensitive personal information during the application process. To learn more on how to recognize job scams, please visit the Federal Trade Commission’s guide at <a href="https://consumer.ftc.gov/articles/job-scams." target="_blank" rel="noopener noreferrer ugc">https://consumer.ftc.gov/articles/job-scams.</a> 
If you believe you’ve been targeted by a recruitment scam, please report it to Innodata at <a href="mailto:verifyjoboffer@innodata.com" target="_blank">verifyjoboffer@innodata.com</a> and consider reporting it to the FTC at <a href="http://reportfraud.ftc.gov/" target="_blank" rel="noopener noreferrer ugc">ReportFraud.ftc.gov</a>.
</div>

Senior Full-Stack Software Engineer

<div>Innodata (Nasdaq: INOD) is a global data engineering company. We believe that data and Artificial Intelligence (AI) are inextricably linked. Our mission is to enable the responsible advancement of artificial intelligence by providing the data, evaluation frameworks, and human expertise required to build AI systems that can be trusted at scale. We provide a range of transferable solutions, platforms, and services for Generative AI / AI builders and adopters. In every relationship, we honor our 36+ year legacy delivering the highest quality data and outstanding outcomes for our customers.</div>Scope of the Role: 
Innodata is building a team of Prompt Engineers to leverage large language models (LLMs) to automate and optimize data annotation and human evaluation workflows. In this role, you will design and implement effective prompt strategies that improve accuracy, localization, and cultural alignment in data labeling and translation processes.
Working closely with Product, Data Science, Operations, and client stakeholders, you will translate business requirements into scalable AI-driven solutions. You will identify automation opportunities, develop prompt-based workflows, and continuously measure and refine performance to ensure high standards of quality and reliability.
This position offers the opportunity to directly impact efficiency, scalability and innovation for a leading global technology partner.
What You’ll Own:
<ul>
<li>Collaborate with data scientists, linguists, and localization experts to ensure accuracy and cultural relevance. </li>
<li>Prototype and validate AI models to demonstrate initial feasibility, potential impact, and overall effectiveness. </li>
<li>Design, develop, and implement prompts for data labeling and localization processes within software applications. </li>
<li>Understand the current components of the software stack, use cases and problems and iterate on solutions leveraging a solid knowledge of data structures, data formats, and data modeling. </li>
<li>Conduct user testing and feedback analysis to optimize prompt design for data accuracy and linguistic consistency. </li>
<li>Analyze model performance using key performance indicators (KPIs) and metrics, ensuring that AI models meet customer acceptance criteria and deliver high-quality outputs. </li>
<li>Communicate technical findings and solution strategies to both technical and non-technical stakeholders, including presenting model performance and actionable insights in a clear, accessible manner. </li>
<li>Collaborate on data pipelines and workflows that integrate LLMs into automated systems, enhancing both the efficiency and effectiveness of data annotation tasks. </li>
<li>Create guidelines and training materials for prompt usage in data labeling and localization projects. </li>
<li>Stay informed on data labeling and localization industry trends and tools to enhance prompt engineering techniques. </li>
</ul>
You’ll Thrive in This Role If You Have:
<ul>
<li>2 years of prompt engineering / LLM fine-tuning, or related AI/ML roles. </li>
<li>Familiarity with tools/platforms for annotation and human-in-the-loop workflows (e.g., Labelbox). </li>
<li>Experience designing and automating data annotation workflows. </li>
<li>Knowledge of data annotation and the challenges of scaling human-in-the-loop workflows. </li>
<li>Familiarity with cloud platforms, containerization, and model deployment.</li>
<li>Deep understanding of LLMs (e.g. transformer-based architectures). </li>
<li>Demonstrated experience programmatically using LLMs to automate data labeling, classification, localization and annotation tasks. </li>
<li>Strong expertise in Python for NLU, for data processing &amp; transformation, and for statistical analysis. Familiarity with JSON, Javascript or XML. </li>
<li>Experience with popular frameworks and libraries, including TensorFlow, PyTorch, Jupyter, and other relevant AI/ML tools. </li>
<li>Familiarity with APIs and platforms for working with LLMs (e.g., OpenAI, Hugging Face, etc.). </li>
<li>Knowledge of localization best practices and cultural nuances for different languages and regions. </li>
<li>Strong understanding of LLM evaluation metrics and the ability to assess model reliability, bias, and generalizability. </li>
<li>Experience working with data pipelines, automation tools, and integrating models into production systems to ensure scalable, reliable solutions.</li>
</ul>
The expected salary range for this position is $80,000 – $90,000 CAD per year, based on experience, skills, and qualifications.
 <div>
Please be aware of recruitment scams involving individuals or organizations falsely claiming to represent employers. Innodata will never ask for payment, banking details, or sensitive personal information during the application process. To learn more on how to recognize job scams, please visit the Federal Trade Commission’s guide at <a href="https://consumer.ftc.gov/articles/job-scams." target="_blank" rel="noopener noreferrer ugc">https://consumer.ftc.gov/articles/job-scams.</a> 
If you believe you’ve been targeted by a recruitment scam, please report it to Innodata at <a href="mailto:verifyjoboffer@innodata.com" target="_blank">verifyjoboffer@innodata.com</a> and consider reporting it to the FTC at <a href="http://reportfraud.ftc.gov/" target="_blank" rel="noopener noreferrer ugc">ReportFraud.ftc.gov</a>.
</div>

Gen AI Engineer

<div>Innodata (Nasdaq: INOD) is a global data engineering company. We believe that data and Artificial Intelligence (AI) are inextricably linked. Our mission is to enable the responsible advancement of artificial intelligence by providing the data, evaluation frameworks, and human expertise required to build AI systems that can be trusted at scale. We provide a range of transferable solutions, platforms, and services for Generative AI / AI builders and adopters. In every relationship, we honor our 36+ year legacy delivering the highest quality data and outstanding outcomes for our customers.</div> 
<div>
Scope of the Role: 
Video is where multimodal models are weakest and hardest to grade. Temporal reasoning, long-form understanding, grounding events in time, and holding audio, video, and text together do not fall out of image benchmarks — and the evaluations for them are still immature. Closing that gap is gated as much by how we design data and evaluation as by architecture. Innodata builds that data and those evaluations for the customers and frontier labs advancing video and multimodal models, and we are hiring a Research Scientist to own the science behind it. 
You will partner directly with the customers and frontier labs building video understanding, video-language, and video-generation models, as interested in the data behind them as in the models themselves. Video spans two model families judged in completely different ways: models that understand video — answering questions, localizing events, grounding language in time — where the question is whether the answer is correct; and models that generate it, where fidelity, temporal coherence, and physical plausibility matter and no automatic metric is settled. You own the evaluation science for both, and knowing when model-based scoring can stand in for a human versus when it can't. Your conclusions shape what our partners measure and collect next. 
What You’ll Own:
You will define how Innodata designs, structures, and evaluates video data for video and multimodal models, and you will validate those choices experimentally. Concretely, you will: 
<ul>
<li>Translate the requirements of video and multimodal models — video understanding, temporal and event localization, action recognition, long-form video, video-language models, video generation, cross-modal reasoning, and multimodal retrieval and grounding — into concrete data specifications: modalities, annotation schemas, sampling, and evaluation criteria. </li>
</ul>
<ul>
<li>Build evaluation methodology for video understanding — temporal grounding accuracy, long-context and long-horizon reasoning, and dynamic multi-turn, cross-modal, and retrieval-and-grounding evaluation — clear about when model-based scoring is trustworthy and when a human is needed. </li>
</ul>
<ul>
<li>Build evaluation methodology for video generation — fidelity, temporal coherence, and physical plausibility, including generative video used as a world model — the regime where automatic metrics are weakest and human judgment matters most. </li>
</ul>
<ul>
<li>Decide how existing and incoming video should be structured, enriched, and sampled to extract the most model value from it, including from messy, domain-specific footage. </li>
</ul>
<ul>
<li>Run experiments that prove data decisions matter: fine-tune and evaluate models on Innodata data, with ablations tying specific data choices to measurable improvement. </li>
</ul>
<ul>
<li>Design adversarial and stumping evaluations that surface where video and multimodal systems fail, and turn those failures into better data. </li>
</ul>
<ul>
<li>Publish. Turn what you learn into benchmarks, methodology, and papers that advance the field and earn the trust of the customers and frontier labs we partner with. </li>
</ul>
<ul>
<li>Work with annotation teams, subject-matter experts, and the synthetic-data pipeline to turn specifications into operational collection and labeling plans. </li>
</ul>
 You’ll Thrive in This Role If You Have:
<ul>
<li>Roughly 5+ years of hands-on industry experience in video understanding or multimodal ML. We weight practical experience over formal credentials; a PhD with a compelling, current research agenda can offset the lower end. </li>
</ul>
<ul>
<li>A Bachelor's degree in computer science, electrical engineering, or a related technical or quantitative field is required; an advanced degree (MS or PhD) in a relevant field is preferred. </li>
</ul>
<ul>
<li>Trained and evaluated video or multimodal models yourself, with strong PyTorch fundamentals. </li>
</ul>
<ul>
<li>Fluency in the formats and tooling video work runs on: ffmpeg and decord pipelines, temporal and COCO-style annotation, WebDataset, Parquet and Arrow, and HuggingFace datasets. </li>
</ul>
<ul>
<li>Experience fine-tuning large video or vision-language models with the modern toolchain (HuggingFace transformers, PEFT, efficient inference), and with long-form video, streaming, temporal segmentation, or synthetic video generation. </li>
</ul>
<ul>
<li>A way of thinking in datasets and benchmarks: you have built evaluation sets, calibrated difficulty, and argued about what makes video data good for a given objective. </li>
</ul>
<ul>
<li>A track record the field recognizes: first-author publications or strong open-source contributions at venues such as CVPR, ICCV, ECCV, NeurIPS, or ICLR. </li>
</ul>
<ul>
<li>The ability to work directly with the research scientists at the customers and frontier labs we partner with, and to explain data and modeling decisions clearly to both expert and non-expert audiences, backed by a rigorous, reproducible approach to experiments and documentation. </li>
</ul>
<ul>
<li>Bonus: interest or hands-on experience in responsible-AI evaluation and red-teaming — safety and robustness testing for video and multimodal systems. </li>
</ul>
The expected salary range for this position is $160,000 - $185,000 p/year, based on experience, skills, and qualifications.
</div><div>
Please be aware of recruitment scams involving individuals or organizations falsely claiming to represent employers. Innodata will never ask for payment, banking details, or sensitive personal information during the application process. To learn more on how to recognize job scams, please visit the Federal Trade Commission’s guide at <a href="https://consumer.ftc.gov/articles/job-scams." target="_blank" rel="noopener noreferrer ugc">https://consumer.ftc.gov/articles/job-scams.</a> 
If you believe you’ve been targeted by a recruitment scam, please report it to Innodata at <a href="mailto:verifyjoboffer@innodata.com" target="_blank">verifyjoboffer@innodata.com</a> and consider reporting it to the FTC at <a href="http://reportfraud.ftc.gov/" target="_blank" rel="noopener noreferrer ugc">ReportFraud.ftc.gov</a>.
</div>

Research Scientist, Video & Multimodal

<div>Innodata (Nasdaq: INOD) is a global data engineering company. We believe that data and Artificial Intelligence (AI) are inextricably linked. Our mission is to enable the responsible advancement of artificial intelligence by providing the data, evaluation frameworks, and human expertise required to build AI systems that can be trusted at scale. We provide a range of transferable solutions, platforms, and services for Generative AI / AI builders and adopters. In every relationship, we honor our 36+ year legacy delivering the highest quality data and outstanding outcomes for our customers.</div>
Scope of the Role: 
Where models actually differ now is robustness across accents, noise, and code-switching; speaker diarization; the naturalness of generated speech; and latency under streaming. Measuring those honestly, and building the data that trains for them, is gated as much by data and evaluation design as by architecture. Innodata builds that data and those evaluations for the customers and frontier labs advancing speech and audio models, and we are hiring a Research Scientist to own the science behind it. 
You will partner directly with the customers and frontier labs building ASR, text-to-speech, speech-to-speech and conversational voice, diarization, and audio-language models, as interested in the data behind them as in the models themselves. Your work is judgment: which conditions and languages a benchmark must cover to be honest, what a transcription convention should be for a given objective, and when an automated metric can be trusted versus when a human ear is required. You will also partner closely with our transcription and linguistics lead, whose standards directly shape what the models learn. 
What You’ll Own:
<ul>
<li>You will define how Innodata designs, structures, and evaluates audio data for speech and audio models, and you will validate those choices experimentally. Concretely, you will: </li>
<li>Translate the requirements of speech and audio models — ASR, text-to-speech and speech generation, speech-to-speech and conversational voice, speaker diarization and verification, audio-language models, and streaming systems — into concrete data specifications: modalities, transcription and annotation schemas, sampling, and evaluation criteria. </li>
</ul>
<ul>
<li>Build evaluation methodology that goes past word error rate — semantic accuracy, robustness to noise and accent, code-switching, diarization error rate (DER), naturalness and intelligibility of generated speech, and streaming latency — and know when automated metrics hold and when they don't. </li>
</ul>
<ul>
<li>Decide how existing and incoming audio should be structured, enriched, and sampled for coverage that fits the model objective — across languages, accents, and acoustic conditions (studio, real-world, telephonic), speaker demographics, emotional and paralinguistic range, scripted versus spontaneous speech, and single- versus multi-speaker settings, including low-resource and code-switched speech. </li>
</ul>
<ul>
<li>Partner with the transcription and linguistics lead to turn model objectives into transcription specifications, and to quantify how transcription conventions and quality move ASR and speech-model results. </li>
</ul>
<ul>
<li>Partner with the audio solutions and engineering team so the audio we collect is built for the model objective: you specify what good data and evaluation require, and they scope programs with customers and capture audio to spec. </li>
</ul>
<ul>
<li>Run experiments that prove data decisions matter: fine-tune and evaluate models on Innodata data, with ablations tying specific data choices to measurable improvement. </li>
</ul>
<ul>
<li>Design adversarial and stumping evaluations — noisy, accented, and adversarial audio — that surface where speech systems fail, and turn those failures into better data. </li>
</ul>
<ul>
<li>Publish. Turn what you learn into benchmarks, methodology, and papers that advance the field and earn the trust of the customers and frontier labs we partner with. </li>
</ul>
<ul>
<li>Work with annotation teams, subject-matter experts, and the synthetic- and augmented-audio pipeline to turn specifications into operational plans. </li>
</ul>
You’ll Thrive in This Role If You Have:
<ul>
<li>Roughly 5+ years of hands-on industry experience in speech or audio ML. We weight practical experience over formal credentials; a PhD with a compelling, current research agenda can offset the lower end. </li>
</ul>
<ul>
<li>A Bachelor's degree in computer science, electrical engineering, or a related technical or quantitative field is required; an advanced degree (MS or PhD) in a relevant field is preferred. </li>
</ul>
<ul>
<li>Trained and evaluated speech or audio models yourself — ASR, TTS, speech-to-speech, speaker, or audio-language models — with strong PyTorch fundamentals. </li>
</ul>
<ul>
<li>Fluency in the toolchains and metrics speech work runs on: ESPnet, NeMo, SpeechBrain, or Kaldi, HuggingFace, forced alignment, and WER/CER and the metrics that go beyond them. </li>
</ul>
<ul>
<li>Hands-on experience with multilingual, accented, dialectal, low-resource, or code-switched speech, and with synthetic or augmented audio (TTS pipelines, noise and room-response simulation). </li>
</ul>
<ul>
<li>A way of thinking in datasets: you have built evaluation sets, reasoned about coverage across conditions, and argued about what makes speech data good for a given objective. </li>
</ul>
<ul>
<li>A track record the field recognizes: first-author publications or strong open-source contributions at venues such as Interspeech, ICASSP, ASRU, SLT, or NeurIPS. </li>
</ul>
<ul>
<li>The ability to work directly with the research scientists at the customers and frontier labs we partner with, and to explain data and modeling decisions clearly to both expert and non-expert audiences, backed by a rigorous, reproducible approach to experiments and documentation. </li>
</ul>
<ul>
<li>Bonus: interest or hands-on experience in responsible-AI evaluation and red-teaming — for example spoofing and voice-cloning robustness, or bias across accents and languages. </li>
</ul>
 
The expected salary range for this position is $160,000 - $185,000 p/year, based on experience, skills, and qualifications.
<div>
Please be aware of recruitment scams involving individuals or organizations falsely claiming to represent employers. Innodata will never ask for payment, banking details, or sensitive personal information during the application process. To learn more on how to recognize job scams, please visit the Federal Trade Commission’s guide at <a href="https://consumer.ftc.gov/articles/job-scams." target="_blank" rel="noopener noreferrer ugc">https://consumer.ftc.gov/articles/job-scams.</a> 
If you believe you’ve been targeted by a recruitment scam, please report it to Innodata at <a href="mailto:verifyjoboffer@innodata.com" target="_blank">verifyjoboffer@innodata.com</a> and consider reporting it to the FTC at <a href="http://reportfraud.ftc.gov/" target="_blank" rel="noopener noreferrer ugc">ReportFraud.ftc.gov</a>.
</div>

Research Scientist, Speech & Audio

<div>Innodata (Nasdaq: INOD) is a global data engineering company. We believe that data and Artificial Intelligence (AI) are inextricably linked. Our mission is to enable the responsible advancement of artificial intelligence by providing the data, evaluation frameworks, and human expertise required to build AI systems that can be trusted at scale. We provide a range of transferable solutions, platforms, and services for Generative AI / AI builders and adopters. In every relationship, we honor our 36+ year legacy delivering the highest quality data and outstanding outcomes for our customers.</div> 
Scope of the Role: 
Progress in physical AI is gated as much by data design as by model architecture. The best manipulation and locomotion policies, vision-language-action models, and world models are only as good as the trajectories, demonstrations, and environments they learn from — so the highest-leverage decisions are made long before a model trains: what to capture, what to synthesize, how to represent an action, and how to know whether any of it will transfer to a real robot. Innodata is building the data and evaluation practice behind the next generation of robotics foundation models, in direct partnership with the customers and frontier labs defining that frontier. We are hiring a Research Scientist to own the science of that data. 
You will partner directly with the customers and frontier labs building robot foundation models, as interested in the data those models learn from as in the models themselves. Your leverage is scientific judgment about data: deciding what is worth capturing in the real world versus generating in simulation, how to weight a training mix across embodiments and sensors, and how to measure whether a policy will hold up outside the lab. Your conclusions will shape what our partners collect next. 
What You’ll Own:
<ul>
<li>You will define how Innodata designs, structures, and evaluates data for robot foundation models, and you will validate those choices experimentally. Concretely, you will: </li>
<li>Translate the requirements of robotics foundation models — vision-language-action models, world models, and manipulation and locomotion policies — into concrete data specifications: modalities, action representations (tokenization, chunking, diffusion and flow-matching action experts), sampling, annotation schemas, and evaluation criteria. </li>
</ul>
<ul>
<li>Decide what is worth capturing in the real world versus generating in simulation, and curate and weight training mixes across heterogeneous robot datasets that span different embodiments, action spaces, and sensor setups. </li>
</ul>
<ul>
<li>Guide collection across capture modalities — motion capture, egocentric, exocentric, teleoperation, and multi-sensor — and across synthetic pipelines, so that what we produce maps cleanly to model needs rather than accumulating for its own sake. </li>
</ul>
<ul>
<li>Build evaluation and benchmarking methodology that predicts real-world transfer — coverage, discriminative power, reliability, and sim-to-real fidelity — including world-model evaluation (rollout quality and action-conditioned prediction) and the domain-randomization and system-identification choices that close the sim-to-real gap. </li>
</ul>
<ul>
<li>Run the experiments that prove it: fine-tune and evaluate foundation models on Innodata data, with data-quality ablations and scaling studies that demonstrate specific data decisions produce measurable model improvement. </li>
</ul>
<ul>
<li>Design adversarial and long-horizon evaluations that surface where policies and world models break, and turn those failure modes into better data. </li>
</ul>
<ul>
<li>Publish. Turn what you learn into benchmarks, methodology, and papers that advance the field and earn the trust of the customers and frontier labs we partner with. </li>
</ul>
<ul>
<li>Collaborate with the capture lab, annotation teams, and the synthetic-data pipeline to turn specifications into operational collection and labeling plans. </li>
</ul>
You’ll Thrive in This Role If You Have:
<ul>
<li>Roughly 4+ years of hands-on industry experience in robot learning or robotics ML. We weight practical experience over formal credentials; because the field is young, a PhD with a compelling, current research agenda can offset the lower end, and we will also consider very senior candidates with the right background. </li>
</ul>
<ul>
<li>A Bachelor's degree in computer science, electrical engineering, robotics, or a related technical field is required; an advanced degree (MS or PhD) in a relevant field is preferred. </li>
</ul>
<ul>
<li>Trained and evaluated robot policies yourself — manipulation or locomotion via imitation learning or reinforcement learning — with strong PyTorch fundamentals. You build and measure models, you don't just call them. </li>
</ul>
<ul>
<li>A way of thinking in datasets: you have curated, filtered, and weighted robot data across embodiments and sensors, and you have real opinions about what makes it good for a given objective. </li>
</ul>
<ul>
<li>Fluency in robotics data formats and standards — the LeRobot dataset format, RLDS, and Open X-Embodiment — along with common motion and sensor formats. </li>
</ul>
<ul>
<li>Hands-on experience with simulation and synthetic data — NVIDIA Isaac Sim, Isaac Lab, and Omniverse, or MuJoCo and comparable engines — including domain randomization, system identification, and sim-to-real transfer. </li>
</ul>
<ul>
<li>Experience with teleoperation or egocentric data collection, and comfort adapting VLM backbones for control and fine-tuning large VLA models with the modern toolchain (HuggingFace transformers, PEFT). </li>
</ul>
<ul>
<li>A track record the field recognizes: first-author publications or strong open-source contributions at venues such as CoRL, ICRA, IROS, RSS, or NeurIPS. </li>
</ul>
<ul>
<li>The ability to work directly with the research scientists at the customers and frontier labs we partner with, and to explain data and modeling decisions clearly to both expert and non-expert audiences, backed by a rigorous, reproducible approach to experiments and documentation. </li>
</ul>
<ul>
<li>Bonus: interest or hands-on experience in responsible-AI evaluation and red-teaming for embodied systems, for example safety and robustness testing. </li>
</ul>
 
The expected salary range for this position is $160,000 - $185,000 p/year, based on experience, skills, and qualifications.
 
 
 <div>
Please be aware of recruitment scams involving individuals or organizations falsely claiming to represent employers. Innodata will never ask for payment, banking details, or sensitive personal information during the application process. To learn more on how to recognize job scams, please visit the Federal Trade Commission’s guide at <a href="https://consumer.ftc.gov/articles/job-scams." target="_blank" rel="noopener noreferrer ugc">https://consumer.ftc.gov/articles/job-scams.</a> 
If you believe you’ve been targeted by a recruitment scam, please report it to Innodata at <a href="mailto:verifyjoboffer@innodata.com" target="_blank">verifyjoboffer@innodata.com</a> and consider reporting it to the FTC at <a href="http://reportfraud.ftc.gov/" target="_blank" rel="noopener noreferrer ugc">ReportFraud.ftc.gov</a>.
</div>

Innodatainc

Open jobs at Innodatainc

Senior Full-Stack Software Engineer

Gen AI Engineer

Research Scientist, Video & Multimodal

Research Scientist, Speech & Audio

Research Scientist, Robotics & World Models

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