Machine Learning / Reinforcement Learning Infrastructure Engineer
Ekarobotics · Boston Area
About this role
## Overview Eka Robotics is on a mission to build intelligence for the physical world—robots that are fast, general, and reliable. We’re scaling our R&D effort and hiring a **Reinforcement/Machine Learning Infrastructure Engineer** to shape our training infrastructure. In this role, you’ll design, implement, and maintain large-scale model training systems that power next-generation robot learning. You’ll focus on building an exceptional developer experience through intuitive, efficient tooling—helping researchers and engineers move from prototyping to large-scale distributed training with minimal friction. ## Responsibilities - **Own Training Infrastructure:** Design, implement, and maintain robust systems for large-scale model training, including job orchestration, scheduling, checkpointing, and experiment tracking. - **Developer Experience & Tooling:** Build streamlined abstractions for launching, monitoring, debugging, and reproducing experiments. - **Scale Distributed Training:** Collaborate with researchers to reliably scale reinforcement learning and machine learning pipelines across compute clusters. - **Resource Management:** Ensure efficient allocation and utilization of cloud-based compute resources. - **Collaborate with Researchers:** Partner with the research team to understand needs, support cutting-edge methods, guide best practices for training at scale, and contribute to core **JAX** model and training code. ## Minimum Qualifications - **Education:** BS, MS, or higher in Computer Science, Computer Engineering, Machine Learning, or a related technical field. - **Software Engineering:** Strong fundamentals with a proven track record building ML training infrastructure, internal developer platforms, or scalable systems. - **Deep Learning Frameworks:** Hands-on experience with large-scale training using **JAX (preferred)**, **PyTorch**, or **TensorFlow**. - **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**). - **Communication & Ownership:** Strong cross-functional communication, ownership mindset, and passion for improving developer experience. - **Infrastructure & DevOps:** Experience building automated testing pipelines, **CI/CD** for ML workflows, and custom logging/telemetry stacks. ## Preferred Qualifications - **Domain Experience:** Background in robotics, reinforcement learning, or other machine learning systems. - **Systems Design:** Experience designing abstractions that balance researcher flexibility with system reliability.
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