Ekarobotics

<h2>Eka Robotics</h2>Eka Robotics is on a mission to build intelligence for the physical world - robots that are fast, general, and reliable. Our approach, grounded in physics, unlocks superhuman capabilities. We are defining the frontier of robotics research and deployment.Our team consists of pioneers in robotics and machine learning. We are now hiring to scale our R&amp;D effort. We are looking for hands-on individuals who are excited to help shape the future of robotics.<h2>Responsibilities</h2><ul><li>Stack Optimization: Maintain a deep understanding of the full robotics software stack; profile and optimize data flows to minimize end-to-end latencies across components.</li><li>Observability &amp; Diagnostics: Design and implement logging and monitoring frameworks to facilitate rapid debugging and provide visibility into system dataflows.</li><li>Testing &amp; Health Monitoring: Develop testing suites and validation pipelines specifically designed to identify robot degradation and anomalies.</li><li>System Identification: Design and execute experiments for system identification to refine simulation models and controller performance.</li><li>ML Deployment &amp; Developer Experience: Build streamlined deployment systems for machine learning models, prioritizing intuitive tooling that simplifies prototyping and minimizes friction for research teams.</li><li>Production-Level C++/python: Author and maintain high-performance, production-grade C++ codebases adhering to industry best practices, and use Python for tooling, data analysis, and scripting.</li><li>Performance &amp; Scalability: Proactively identify bottlenecks in performance and scalability, driving iterative enhancements to the software architecture.</li><li>Safety Integration: Design and implement safety functions while ensuring their compatibility with machine learning models.</li></ul><h2>Minimum Qualifications</h2><ul><li>Education: BS, MS or higher in Computer Science, Robotics, Computer Engineering, or a related technical field.</li><li>C++ Expertise: 3+ years of professional experience writing production-quality C++, with a focus on memory management, multi-threading, and real-time constraints.</li><li>Robotics Frameworks: Hands-on experience with ROS2 or similar robotic middleware, including custom message generation and lifecycle management.</li><li>Systems Engineering: Strong understanding of Linux systems, including profiling tools to identify and resolve latency bottlenecks.</li><li>ML Deployment: Experience deploying and optimizing machine learning models (TensorRT, ONNX) onto edge devices or robotic platforms.</li></ul><h2>Preferred Qualifications</h2><ul><li>Safety Critical Systems: Experience implementing safety functions or working with functional safety standards.</li><li>Math &amp; Physics: Strong foundational knowledge of system identification, control theory, and rigid body dynamics.</li><li>Infrastructure &amp; DevOps: Experience building automated testing pipelines, CI/CD for hardware, and custom logging/telemetry/visualization stacks.</li><li>Simulation: Proficiency in using simulation environments for system validation and testing.</li></ul>

Robotics Software Engineer

<h2>Eka Robotics</h2>Eka Robotics is on a mission to build intelligence for the physical world - robots that are fast, general, and reliable. Our approach, grounded in physics, unlocks superhuman capabilities. We are defining the frontier of robotics research and deployment.Our team consists of pioneers in robotics and machine learning. We are now hiring to scale our R&amp;D effort. We are looking for hands-on individuals who are excited to help shape the future of robotics.<h2>Responsibilities</h2><ul><li>Evaluate and extend physics engines (e.g. MuJoCo, Newton)</li><li>Scale and optimize GPU-based physics simulators for massive training workloads</li><li>Improve the realism of contact-rich manipulation environments</li><li>Create tools and automated workflows to validate, tune, and optimize physical and visual assets</li></ul><h2>Minimum Qualifications</h2><ul><li>Bachelor’s degree or higher in Computer Science, Robotics, Engineering, or a related field</li><li>Strong C++ and Python programming skills</li><li>Hands-on experience with physics engines</li><li>Proven ability to build and scale high-performance, physics-based simulation environments</li></ul><h2>Preferred Qualifications</h2><ul><li>Strong mathematical &amp; physics background – particularly expertise in rigid/soft-body dynamics, contact mechanics, FEA, and numerical stability</li><li>Experience across the entire simulation stack, from CAD import and mesh optimization to real-time rendering</li><li>Experience with reinforcement learning, policy training, differentiable physics, or neural simulation models</li><li>Background in contact-rich manipulation or legged locomotion</li><li>Strong knowledge of GPU acceleration and robotics model formats (URDF, SDF, USD)</li><li>Proven success deploying simulation-trained policies onto real-world hardware</li></ul>

Research Engineer – Simulation

Eka RoboticsEka Robotics is on a mission to build intelligence for the physical world - robots that are fast, general, and reliable. Our approach, grounded in physics, unlocks superhuman capabilities. We are defining the frontier of robotics research and deployment.Our team consists of pioneers in robotics and machine learning. We are now hiring to scale our R&amp;D effort. We are looking for hands-on individuals who are excited to help shape the future of robotics.Responsibilities<ul><li>Build computer vision and visual representation learning pipelines for robotic manipulation, including RGB, RGB-D, depth, segmentation, pose, keypoint, and object-centric representations.</li><li>Develop visual models that support reinforcement learning and imitation learning policies, including end-to-end visuomotor policies that map visual observations to robot actions.</li><li>Improve our data pipeline for vision-based manipulation policies through domain randomization, photorealistic rendering, synthetic data generation, sensor noise modeling, and real-world fine-tuning.</li><li>Design and train perception models that are robust to lighting changes, camera viewpoint shifts, texture variation, clutter, occlusion, object instance variation, and imperfect calibration.</li><li>Evaluate learned visual representations and policies on real robotic manipulation tasks, identify failure modes, and iterate on models, data, and training procedures.</li><li>Collaborate with robotics, robot learning, and simulation engineers to define the perception strategy for robotic manipulation.</li><li>Set up, calibrate, and evaluate camera and depth sensing systems when needed, with an emphasis on how sensor choices affect learned policies and real-world robustness.</li></ul>Minimum Qualifications<ul><li>Ph.D. in computer vision or 3+ years of experience working on a computer vision product.</li><li>Strong background in machine learning for computer vision, especially deep learning-based visual perception.</li><li>Experience training modern computer vision models in Jax, PyTorch or similar frameworks.</li><li>Practical experience with visual representation learning, object detection, segmentation, pose estimation, depth estimation, tracking, or 3D perception.</li><li>Strong Python programming skills</li><li>Ability to move fluidly between research code and production-quality systems.</li><li>Strong understanding of how data distribution, sensor noise, calibration, lighting, and scene variation affect model performance.</li></ul>Preferred Qualifications<ul><li>Experience training policies from visual observations, including RGB, RGB-D, point clouds, object-centric representations, or learned latent representations.</li><li>Experience with domain randomization, synthetic data generation, differentiable rendering, neural rendering, or photorealistic simulation.</li><li>Experience with robotics simulators or synthetic data tools such as Isaac Sim, MuJoCo or similar environments.</li><li>Familiarity with robot learning methods such as reinforcement learning, behavior cloning, diffusion policies, offline RL, or learning from demonstrations.</li><li>Experience with real robot deployment, including camera calibration, hand-eye calibration, depth sensors, ROS/ROS2, or robot data collection pipelines.</li><li>First-author publications in top computer vision, robotics, or machine learning venues such as CVPR, ICCV, ECCV, NeurIPS, ICLR, ICML, RSS, CoRL, ICRA, or IROS.</li></ul>

Machine Learning / Computer Vision Engineer

Eka RoboticsEka Robotics is on a mission to build intelligence for the physical world - robots that are fast, general, and reliable. Our approach, grounded in physics, unlocks superhuman capabilities. We are defining the frontier of robotics research and deployment.Our team consists of pioneers in robotics and machine learning. We are now hiring to scale our R&amp;D effort. We are looking for hands-on individuals who are excited to help shape the future of robotics.The RoleWe are looking for a Reinforcement/Machine Learning Infrastructure Engineer to shape our training infrastructure. In this role, you will be responsible for designing, implementing, and maintaining the large-scale model training systems that power our next generation of robot learning.We believe that world-class infrastructure is the foundation for moving research into production. You will focus on building an exceptional developer experience, creating intuitive and efficient tooling that our engineers and scientists love to use. Your work will directly accelerate our research cycles, making it effortless to test new ideas and scale successful experiments into production training runs. You will work closely with researchers to ensure our infrastructure scales seamlessly from prototyping to large-scale distributed training.This is a hands-on, high-impact role at the intersection of machine learning, software engineering, and scalable infrastructure.Responsibilities<ul><li>Own Training Infrastructure: Design, implement, and maintain robust systems for large-scale model training, including job orchestration, scheduling, checkpointing, and experiment tracking.</li><li>Developer Experience &amp; Tooling: Build streamlined, intuitive abstractions for launching, monitoring, debugging, and reproducing experiments, minimizing friction and maximizing productivity for our research teams.</li><li>Scale Distributed Training: Work closely with researchers to reliably scale reinforcement learning and machine learning pipelines across compute clusters.</li><li>Resource Management: Ensure efficient allocation and utilization of cloud-based compute resources while building the foundational systems needed for future scaling.</li><li>Collaborate with Researchers: Partner with the research team to understand their needs, build infrastructure that supports cutting-edge methods, guide best practices for training at scale, and contribute to core JAX model and training code.</li></ul>Minimum Qualifications<ul><li>Education: BS, MS or higher in Computer Science, Computer Engineering, Machine Learning or a related technical field.</li><li>Software Engineering: Strong software engineering fundamentals with a proven track record of building ML training infrastructure, internal developer platforms, or scalable systems.</li><li>Deep Learning Frameworks: Hands-on experience with large-scale training using JAX (preferred), PyTorch, or TensorFlow.</li><li>Distributed Systems: Familiarity with distributed training, multi-host setups, data pipelines, and managing workloads on cloud platforms or orchestration systems (e.g., Kubernetes, SLURM, GCP, AWS).</li><li>Communication &amp; Ownership: Strong cross-functional communication skills, a deep ownership mindset, and a passion for building tools that improve the developer experience.</li><li>Infrastructure &amp; DevOps: Experience building automated testing pipelines, CI/CD for ML workflows, and custom logging/telemetry stacks.</li></ul>Preferred Qualifications<ul><li>Domain Experience: Background in robotics, reinforcement learning or other machine learning systems.</li><li>Systems Design: Experience designing abstractions that balance researcher flexibility with system reliability.</li></ul>

Machine Learning / Reinforcement Learning Infrastructure Engineer

Eka RoboticsEka Robotics is on a mission to build intelligence for the physical world - robots that are fast, general, and reliable. Our approach, grounded in physics, unlocks superhuman capabilities. We are defining the frontier of robotics research and deployment.Our team consists of pioneers in robotics and machine learning. We are now hiring to scale our R&amp;D effort. We are looking for hands-on individuals who are excited to help shape the future of robotics.Responsibilities<ul><li>Algorithm Development: Research and implement reinforcement learning and supervised learning algorithms for robotic manipulation.</li><li>Simulation: Design simulation models and domain randomization strategies; collaborate with the robotics team to ensure alignment with physical systems.</li><li>Performance Optimization: Design experiments to evaluate and optimize model architectures for sample complexity and policy performance with real-time execution constraints.</li><li>Data &amp; Pipeline Engineering: Develop scalable data management pipelines for real and synthetic data; evaluate and select algorithms that maximize data efficiency and overall policy performance.</li><li>On-Robot Evaluation: Deploy, evaluate, and debug policies on physical hardware; identify bottlenecks and implement improvements in collaboration with the robotics team.</li></ul>Qualifications<ul><li>Education: BS, MS, or PhD in Computer Science, Robotics, or a related field.</li><li>Core Expertise: Deep theoretical and practical knowledge of reinforcement learning and supervised learning algorithms.</li><li>Robotics Toolkit: Experience with physics engines (e.g., Isaac Sim, MuJoCo, PyBullet) and robotics middleware (ROS/ROS2).</li><li>Architectural Depth: A deep understanding of modern architectures, including Transformers, CNNs, and Foundation Models.</li><li>Technical Proficiency: Expert-level Python skills and proficiency in deep learning frameworks such as PyTorch or JAX.</li><li>Engineering Rigor: A strong commitment to clean code, version control, and reproducible experimental workflows.</li><li>Track Record: A history of publications in top-tier robotics or machine learning conferences, or a portfolio of projects providing strong practical evidence of expertise in the field.</li></ul>

Ekarobotics

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Robotics Software Engineer

Research Engineer – Simulation

Machine Learning / Computer Vision Engineer

Machine Learning / Reinforcement Learning Infrastructure Engineer

Machine Learning / Reinforcement Learning Engineer

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