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Open nowPosted 585 days ago

Machine Learning / Reinforcement Learning Infrastructure Engineer

Eka8 open roles

Where
Boston Area
Work mode
On site
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Your applicationOpen nowMachine Learning / Reinforcement Learning Infrastructure EngineerEka · Boston Area

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The clock on this job

Early applications get read.

9.0% of postings close within 7 days. Measured by our own scanner across the market. Eka postings stay open a median of 13 days.

Share of postings closed within
  1. 1.8%1 day
  2. 3.9%3 days
  3. 9.0%7 days
  4. 15.5%14 days
  5. 34.0%30 days
This job: posted 585 days ago

Eka median: 13 days open

The posting

Eka Robotics

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&D effort. We are looking for hands-on individuals who are excited to help shape the future of robotics.

The Role

We 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

- 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, intuitive abstractions for launching, monitoring, debugging, and reproducing experiments, minimizing friction and maximizing productivity for our research teams.

- Scale Distributed Training: Work closely 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 while building the foundational systems needed for future scaling.

- 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.

Minimum Qualifications

- Education: BS, MS or higher in Computer Science, Computer Engineering, Machine Learning or a related technical field.

- Software Engineering: Strong software engineering fundamentals with a proven track record of 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 skills, a deep ownership mindset, and a passion for building tools that improve the 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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