over 1 year ago
Boston, MA, USASenior
Responsibilities
- Design, implement, and maintain large-scale model training infrastructure for job orchestration, scheduling, checkpointing, and experiment tracking.
- Build intuitive tooling for launching, monitoring, debugging, and reproducing research experiments.
- Scale reinforcement learning and machine learning pipelines across distributed compute clusters.
- Manage the efficient allocation and utilization of cloud-based compute resources.
- Partner with researchers to develop scalable training infrastructure, guide training-at-scale best practices, and contribute to core JAX model and training code.
- Build automated testing pipelines, CI/CD workflows for machine learning, and custom logging and telemetry systems.
Requirements
- Bachelor’s degree or higher in Computer Science, Computer Engineering, Machine Learning, or a related technical field.
- Strong software engineering fundamentals and a proven track record building ML training infrastructure, internal developer platforms, or scalable systems.
- Hands-on experience with large-scale training using JAX, PyTorch, or TensorFlow.
- Familiarity with distributed training, multi-host systems, data pipelines, and workloads on cloud platforms or orchestration systems such as Kubernetes, SLURM, GCP, or AWS.
- Strong cross-functional communication, ownership, and developer-experience orientation.
- Preferred experience in robotics, reinforcement learning, or other machine learning systems.
- Preferred experience designing abstractions that balance researcher flexibility with system reliability.
Tech Stack
Categories
About Eka
Eka Robotics develops general-purpose robotic systems and autonomy software to bring reliable, high‑speed automation to real‑world environments. The company applies physics‑based methods alongside modern AI to build and deploy robots that can handle varied tasks across industries. Privately held and based in Cambridge, Massachusetts, it focuses on advancing and commercializing robotic intelligence for practical deployment.