Togetherai

<h3><strong>About the Role </strong></h3>
<p>We are seeking a highly analytical and detail-oriented Data Analytics Engineer to join our data team. This role will report into the Revenue Operations team. The ideal candidate will help build and maintain the data infrastructure and reporting used across go-to-market, finance, and product teams, working closely with analytics colleagues across both the GTM and core data teams to deliver reliable, well-documented data products.</p>
<h3><strong>Responsibilities</strong></h3>
<ul>
<li>Develop and maintain dashboards and reports primarily supporting GTM stakeholders, with additional use by Finance, Product, and other cross-functional teams; assist with deep-dive analyses on business performance and trends.</li>
<li>Pull and analyze data from source systems (Salesforce, Amplitude, production systems, billing) to support reporting and ad hoc requests.</li>
<li>Use SQL to extract, clean, and analyze data, with attention to correctness and efficiency.</li>
<li>Build foundational SQL and business knowledge and quickly grow into contributing to dbt models within Snowflake, following established dimensional modeling standards.</li>
<li>Partner with GTM, Finance, and Product stakeholders to understand data needs and keep reporting and definitions consistent.</li>
</ul>
<h3><strong>Requirements</strong></h3>
<ul>
<li>Bachelor's degree in Business Analytics, Data Science, Statistics, Computer Science, or a related quantitative field.</li>
<li>1-3 years of experience in a Data Analyst, BI, or Analytics Engineering role.</li>
<li>Strong SQL skills required, with the ability to write complex, multi-step queries involving joins and aggregations; experience with dbt and/or Python is a strong plus.</li>
<li>Familiarity with a cloud data warehouse (Snowflake preferred) and BI tools (Hex, Metabase).</li>
<li>Strong attention to detail and ability to manage multiple priorities in a fast-paced environment.</li>
<li>Excellent communication skills, with the ability to explain data findings to non-technical stakeholders.</li>
</ul>
<h3>About Together AI</h3>
<p>Together AI is a research-driven artificial intelligence company. We believe open and transparent AI systems will drive innovation and create the best outcomes for society, and together we are on a mission to significantly lower the cost of modern AI systems by co-designing software, hardware, algorithms, and models. We have contributed to leading open-source research, models, and datasets to advance the frontier of AI, and our team has been behind technological advancement such as FlashAttention, Hyena, FlexGen, and RedPajama. We invite you to join a passionate group of researchers in our journey in building the next generation AI infrastructure.</p>
<h3>Compensation</h3>
<p>We offer competitive compensation, startup equity, health insurance and other competitive benefits. The US base salary range for this full-time position is: $ 120K - $150K + equity + benefits. Our salary ranges are determined by location, level and role. Individual compensation will be determined by experience, skills, and job-related knowledge.</p>
<h3>Equal Opportunity</h3>
<p>Together AI is an Equal Opportunity Employer and is proud to offer equal employment opportunity to everyone regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status, and more.</p>
<p>Please see our privacy policy at<a href="https://www.together.ai/privacy" rel="noopener noreferrer ugc" target="_blank"> https://www.together.ai/privacy</a>. </p>

GTM Data Analytics Engineer

<h3><strong>About the role</strong></h3>
<p>The Model Shaping team at Together AI works on products and research focused on tailoring open foundation models to downstream applications. We build services that enable machine learning developers to choose the best models for their tasks and further improve these models using domain-specific data. In addition, we develop new methods for more efficient model training and evaluation, drawing inspiration from a broad range of ideas across machine learning, natural language processing, and ML systems.</p>
<p>As a Research Engineer within Model Shaping, you will develop a platform that enables users to customize open-source models with their own data. Working across the training and inference stacks, you will build and improve our Fine-Tuning, Reinforcement Learning, and Evaluation services – from ensuring a seamless path from post-training to production serving, to optimizing the inference engine for RL training workloads. You will collaborate closely with our product, research, and engineering teams to keep the API reliable, performant, and well integrated into the company's technical infrastructure. Above all, you will help build the foundational layer of the open-source AI ecosystem, enabling developers around the world to efficiently create high-quality models tailored to their specific applications.</p>
<h3><strong>Responsibilities</strong></h3>
<ul>
<li>Design and build Together’s systems for customizing open-source models</li>
<li>Build integrations between the Model Shaping and Inference platforms to ensure a seamless path from post-training to serving production workloads</li>
<li>Add features to inference engines for large-scale post-training experiments, including optimizations for RL workloads</li>
<li>Make sure the service is stable and robust, participating in an on-call rotation and ensuring 24/7 availability of our platform</li>
</ul>
<h3><strong>Requirements</strong></h3>
<ul>
<li>Have 2+ years of experience building and deploying machine learning-based services in a production environment</li>
<li>Have hands-on experience with modern inference engines, such as SGLang, vLLM, and TensorRT-LLM</li>
<li>Are familiar with the latest methods for fine-tuning LLMs and other AI models</li>
<li>Have a strong software engineering background in Python or Go</li>
<li>Stay up to date with the latest advances and trends in the machine learning community</li>
</ul>
<h3><strong>Experience in any of the following will make you stand out</strong></h3>
<ul>
<li>Serving low-precision (FP4/FP8) models, multiple LoRA adapters within one model instance (Multi-LoRA), or models distributed across several GPU nodes</li>
<li>Optimizing the performance of RL training workloads</li>
<li>Developing CUDA/Triton/CuTE DSL kernels for inference</li>
<li>Developing large-scale and high-load production systems</li>
<li>Maintaining or contributing to open-source ML projects</li>
<li>Managing machine learning workloads on Kubernetes clusters</li>
</ul>
<h3><strong>About Together AI</strong></h3>
<p>Together AI is a research-driven artificial intelligence company. We believe open and transparent AI systems will drive innovation and create the best outcomes for society, and together we are on a mission to significantly lower the cost of modern AI systems by co-designing software, hardware, algorithms, and models. We have contributed to leading open-source research, models, and datasets to advance the frontier of AI, and our team has been behind technological advancement such as FlashAttention, ATLAS, RedPajama, and Mamba. We invite you to join a passionate group of researchers in our journey in building the next generation AI infrastructure.</p>
<h3><strong>Compensation</strong></h3>
<p>We offer competitive compensation, startup equity, health insurance, and other benefits. The US base salary range for this full-time position is $200,000 - $290,000. Our salary ranges are determined by location, level and role. Individual compensation will be determined by experience, skills, and job-related knowledge.</p>
<h3><strong>Equal Opportunity</strong></h3>
<p>Together AI is an Equal Opportunity Employer and is proud to offer equal employment opportunity to everyone regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status, and more.</p>
<p>Please see our privacy policy at <a href="https://www.together.ai/privacy" rel="noopener noreferrer ugc" target="_blank">https://www.together.ai/privacy</a></p>
<p></p>

Research Engineer, Post-Training Inference

<h3><strong>About the Role</strong></h3>
<p>Together AI is building the best inference infrastructure for voice applications. Our Voice AI platform powers production-grade, real-time voice agents and applications — serving speech-to-text and text-to-speech models with best-in-class latency and reliability.</p>
<p>We're looking for a Staff ML Engineer to drive the model serving layer for voice workloads. You'll work hands-on with inference engines like TRT-LLM and SGLang to optimize how we serve models like Whisper, Parakeet, Orpheus, and Kokoro — pushing latency and throughput to the frontier. You'll profile GPU utilization, design batching strategies for streaming audio, and ensure new model architectures can go from research to production quickly.</p>
<p>This is a foundational hire on a small, high-impact team. Voice inference has unique challenges — streaming audio, tokenization, real-time latency budgets — that require dedicated ML engineering focus. You'll shape how Together serves voice models as the industry moves from pipeline architectures (ASR → LLM → TTS) toward end-to-end speech-to-speech.</p>
<ul>
<li>Own the model serving stack that powers Together's voice platform across STT, TTS, and speech-to-speech.</li>
<li>Work directly with state-of-the-art accelerators (H100s, H200s, B200s) to optimize voice model inference.</li>
<li>Collaborate with model partners (Cartesia, Deepgram, Rime, and others) to bring their models to production on Together's infrastructure.</li>
<li>Build quality evaluation frameworks that guide model selection for customers and inform the roadmap.</li>
<li>Join a small, early-stage team with outsized impact on a fast-growing product area.</li>
</ul>
<p> </p>
<p><strong>Responsibilities</strong></p>
<ul>
<li>Own the voice inference roadmap end-to-end — define and execute the technical strategy for optimizing STT, TTS, and speech-to-speech models across Together's infrastructure, with a clear-eyed view of where the field is heading and how to position the platform ahead of it.</li>
<li>Drive best-in-class inference performance — architect and implement systems targeting leading TTFB, throughput, and GPU utilization for voice workloads; set the performance bar others in the industry measure against, not just catch up to.</li>
<li>Lead productionization of voice models at scale — design the serving architecture for serverless and dedicated endpoints, including batching strategies, streaming inference pipelines, and memory management tailored to real-time audio; own reliability and latency SLAs.</li>
<li>Build the voice evaluation platform — design a rigorous, extensible evaluation framework covering WER across accents, languages, and noise conditions for STT; naturalness, latency, and pronunciation fidelity for TTS; establish the internal benchmark methodology that informs model selection and roadmap decisions.</li>
<li>Shape the architecture for next-generation model support — anticipate and enable emerging model paradigms — audio-native LLMs, codec-based architectures (SNAC, Encodec), and end-to-end speech-to-speech systems — before they're mainstream, not after.</li>
<li>Serve as the technical DRI for model partner integrations — lead deep collaboration with partners such as Cartesia, Deepgram, and Rime; own the full lifecycle from integration to optimization to ongoing performance accountability.</li>
<li>Diagnose and resolve the hardest performance problems in the stack — conduct systematic profiling and root-cause analysis from GPU kernel behavior to framework-level bottlenecks; drive shipped improvements with documented, measurable impact.</li>
<li>Influence platform architecture across the organization — partner with platform engineering leadership to ensure the serving layer is built for the latency and reliability demands of real-time voice APIs; your technical decisions should raise the ceiling for the whole team.</li>
<li>Define and scale voice fine-tuning capabilities — lead the technical direction for enabling customers to fine-tune STT and TTS models on Together's infrastructure, establishing the primitives for differentiated voice experiences.</li>
<li>Lay technical foundations for a category-defining product surface — architect systems with enough foresight that they support multiple new voice products with minimal rework; think in terms of platforms, not point solutions.</li>
</ul>
<p><strong>Requirements</strong></p>
<ul>
<li>8+ years of ML engineering experience, with a demonstrated focus on model serving, inference optimization, or ML infrastructure at production scale — including systems you've owned from design through live traffic.</li>
<li>Deep, practical expertise in LLM serving engines (vLLM, SGLang, TensorRT-LLM, or equivalent) — you've modified engine internals, debugged edge cases under load, and contributed improvements back; you don't stop at the API surface.</li>
<li>Expert-level Python and PyTorch proficiency, with a strong command of GPU optimization — CUDA kernels, memory hierarchies, profiling toolchains — and a track record of turning that knowledge into shipped latency or throughput wins.</li>
<li>Proven system design judgment — you've made architectural decisions that held up at scale and influenced how a team or platform evolved; you can articulate the tradeoffs you made and why.</li>
<li>Strong technical leadership — you operate with high autonomy, define the right problems before solving them, and raise the bar for engineering quality around you without requiring process overhead.</li>
<li>Sharp product intuition for developer tooling — you understand what voice application developers actually need to ship great products, and you let that shape your technical priorities, not just the other way around.</li>
<li>Proven ability to move fast in ambiguous environments — you've thrived on early-stage or platform teams where scope is wide, ownership is deep, and the roadmap you build is the one you execute.</li>
<li>Strong foundation in speech and audio ML (ASR/TTS architectures, audio signal processing) — directly relevant experience is strongly preferred; exceptional ML engineering fundamentals with genuine curiosity about the domain is also considered.</li>
<li>Familiarity with audio codec and tokenization schemes (SNAC, Encodec, DAC) is a meaningful plus at this level.</li>
<li>Experience training or fine-tuning speech models at scale is a significant advantage.</li>
<li>Bachelor's or Master's in Computer Science, Electrical Engineering, or related field — or equivalent depth demonstrated through your work.</li>
</ul>
<h3><strong>About Together AI</strong></h3>
<p>Together AI is a research-driven artificial intelligence company. We believe open and transparent AI systems will drive innovation and create the best outcomes for society, and together we are on a mission to significantly lower the cost of modern AI systems by co-designing software, hardware, algorithms, and models. We have contributed to leading open-source research, models, and datasets to advance the frontier of AI, and our team has been behind technological advancement such as FlashAttention, Hyena, FlexGen, and RedPajama. We invite you to join a passionate group of researchers and engineers in our journey in building the next generation AI infrastructure.</p>
<h3><strong>Compensation</strong></h3>
<p>We offer competitive compensation, startup equity, health insurance and other competitive benefits. The US base salary range for this full-time position is: $220,000 - $280,000 + equity + benefits. Our salary ranges are determined by location, level and role. Individual compensation will be determined by experience, skills, and job-related knowledge.</p>
<h3><strong>Equal Opportunity</strong></h3>
<p>Together AI is an Equal Opportunity Employer and is proud to offer equal employment opportunity to everyone regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status, and more.</p>
<p>Please see our privacy policy at <a href="https://www.together.ai/privacy" rel="noopener noreferrer ugc" target="_blank">https://www.together.ai/privacy</a>  </p>

Staff Machine Learning Engineer, Voice AI 

<h3><strong>About the Role</strong></h3>
<p>Together AI is seeking a Machine Learning Engineer to join our<strong> </strong>Inference Engine team, focusing on optimizing and enhancing the performance of our AI inference systems. This role involves working with state-of-the-art large language models models and ensuring they run efficiently and effectively at scale. If you are passionate about AI inference, PyTorch, and developing high-performance systems, we want to hear from you. This position offers the chance to collaborate closely with AI researchers and engineers to create cutting-edge AI solutions. Join us in shaping the future at Together AI!</p>
<h3><strong>Responsibilities</strong></h3>
<ul>
<li>Design and build the production systems that power the Together AI inference engine, enabling reliability and performance at scale.</li>
<li>Develop and optimize runtime inference services for large-scale AI applications.</li>
<li>Collaborate with researchers, engineers, product managers, and designers to bring new features and research capabilities to the world.</li>
<li>Conduct design and code reviews to ensure high standards of quality.</li>
<li>Create services, tools, and developer documentation to support the inference engine.</li>
<li>Implement robust and fault-tolerant systems for data ingestion and processing.</li>
</ul>
<h3><strong>Requirements</strong></h3>
<ul>
<li>3+ years of experience writing high-performance, well-tested, production-quality code.</li>
<li>Proficiency with Python and PyTorch.</li>
<li>Demonstrated experience in building high performance libraries and tooling.</li>
<li>Excellent understanding of low-level operating systems concepts including multi-threading, memory management, networking, storage, performance, and scale.</li>
<li>Preferred: Knowledge of existing AI inference systems such as TGI, vLLM, TensorRT-LLM, Optimum</li>
<li>Preferred: Knowledge of AI inference techniques such as speculative decoding.</li>
<li>Preferred: Knowledge of CUDA/Triton programming.</li>
<li>Nice to have: Knowledge of Rust, Cython and compilers.</li>
</ul>
<h3><strong>About Together AI</strong></h3>
<p>Together AI is a research-driven artificial intelligence company. We believe open and transparent AI systems will drive innovation and create the best outcomes for society. Together, we are on a mission to significantly lower the cost of modern AI systems by co-designing software, hardware, algorithms, and models. We have contributed to leading open-source research, models, and datasets to advance the frontier of AI. Our team has been behind technological advancements such as FlashAttention, Hyena, FlexGen, and RedPajama. We invite you to join a passionate group of researchers and engineers in our journey to build the next-generation AI infrastructure.</p>
<h3><strong>Compensation</strong></h3>
<p>We offer competitive compensation, startup equity, health insurance, and other competitive benefits. The US base salary range for this full-time position is $160,000 - $230,000 + equity + benefits. Our salary ranges are determined by location, level, and role. Individual compensation will be determined by experience, skills, and job-related knowledge.</p>
<h3><strong>Equal Opportunity</strong></h3>
<p>Together AI is an Equal Opportunity Employer and is proud to offer equal employment opportunities to everyone regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status, and more.</p>
<div>
<div>
<div>
<p>Please see our privacy policy at <a href="https://www.together.ai/privacy" rel="noopener noreferrer ugc" target="_blank">https://www.together.ai/privacy</a>  </p>
</div>
</div>
</div>

Machine Learning Engineer - Inference

<h3><strong>About the Role</strong></h3>
<p>As a Systems Research Engineer specialized in GPU Programming, you will play a crucial role in developing and optimizing GPU-accelerated kernels and algorithms for ML/AI applications. Working closely with the modeling and algorithm team, you will co-design GPU kernels and model architecture to enhance the performance and efficiency of our AI systems. Collaborating with the hardware and software teams, you will contribute to the co-design of efficient GPU architectures and programming models, leveraging your expertise in GPU programming and parallel computing. Your research skills will be vital in staying up-to-date with the latest advancements in GPU programming techniques, ensuring that our AI infrastructure remains at the forefront of innovation.</p>
<h3><strong>Requirements</strong></h3>
<ul>
<li>Strong background in GPU programming and parallel computing, such as CUDA and/or Triton.</li>
<li>Knowledge of ML/AI applications and models</li>
<li>Knowledge of performance profiling and optimization tools for GPU programming</li>
<li>Excellent problem-solving and analytical skills</li>
<li>Bachelor's, Master's, or Ph.D. degree in Computer Science, Electrical Engineering, or equivalent practical experiences</li>
</ul>
<h3><strong>Responsibilities</strong></h3>
<ul>
<li>Optimize and fine-tune GPU code to achieve better performance and scalability</li>
<li>Collaborate with cross-functional teams to integrate GPU-accelerated solutions into existing software systems</li>
<li>Stay up-to-date with the latest advancements in GPU programming techniques and technologies</li>
</ul>
<h3><strong>About Together AI</strong></h3>
<p>Together AI is a research-driven artificial intelligence company. We believe open and transparent AI systems will drive innovation and create the best outcomes for society, and together we are on a mission to significantly lower the cost of modern AI systems by co-designing software, hardware, algorithms, and models. We have contributed to leading open-source research, models, and datasets to advance the frontier of AI, and our team has been behind technological advancement such as FlashAttention, Hyena, FlexGen, and RedPajama. We invite you to join a passionate group of researchers in our journey in building the next generation AI infrastructure.</p>
<h3><strong>Compensation</strong></h3>
<p>We offer competitive compensation, startup equity, health insurance, and other benefits, as well as flexibility in terms of remote work. The US base salary range for this full-time position is: $160,000 - $230,000 + equity + benefits. Our salary ranges are determined by location, level and role. Individual compensation will be determined by experience, skills, and job-related knowledge.</p>
<h3><strong>Equal Opportunity</strong></h3>
<p>Together AI is an Equal Opportunity Employer and is proud to offer equal employment opportunity to everyone regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status, and more.</p>
<p>Please see our privacy policy at <a href="https://www.together.ai/privacy" rel="noopener noreferrer ugc" target="_blank">https://www.together.ai/privacy</a>  </p>
<p> </p>

Togetherai

Open jobs at Togetherai

GTM Data Analytics Engineer

Research Engineer, Post-Training Inference

Staff Machine Learning Engineer, Voice AI

Machine Learning Engineer - Inference

Systems Research Engineer, GPU Programming

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