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Eka

Transport robotics platform with fleet management

Developer Productivity Engineer

Platform EngineeringConfirmed live in the last 24 hours
Full-timeNo salary listedIn-person

About the Job

Requirements

  • Track record of building and maintaining build systems, developer tools, or continuous integration infrastructure used by other engineers.
  • Ability to write clear, maintainable software and treat internal tools as products with users.
  • Deep understanding of dependency graphs, caching, reproducibility, and the tradeoffs involved in speeding up builds and tests.
  • Proficiency with Python, Linux, shell tooling, and debugging problems across language and system boundaries.
  • Ability to take an ambiguous problem, work with its users, and carry a solution through implementation and adoption.

Responsibilities

  • Own and evolve the Bazel build system, including build rules, toolchains, dependency management, and remote caching.
  • Make builds and tests faster and more reliable across local development and continuous integration.
  • Improve engineering workflows, including development environment setup, debugging failed tests, and packaging training jobs.
  • Build tools that make common tasks straightforward and failures easy to understand.
  • Work with researchers and robotics engineers to identify recurring friction, prioritize improvements, and measure whether improvements help.
  • Keep development environments and build outputs reproducible across Linux, macOS, and GPU-enabled containers.

Desired Qualifications

  • Hands-on experience with Bazel.
  • Experience managing C++ toolchains, Python packaging, GPU software dependencies, or machine learning training infrastructure.
  • Prior robotics or reinforcement learning experience.

About the Company

Eka

Transport robotics platform with fleet management

Eka Robotics builds transport robotics systems with automated guided vehicles, control software, and fleet management to automate material movement. Its platform trains robot controllers in simulation using self-supervised learning and then transfers the policies to real robots through sim-to-real reinforcement learning, giving robots practical intuition for dynamic environments. The company differentiates itself with a strong academic and industry-backed team and a focus on physical AI and end-to-end automation for robotics and drones. Its goal is to enable autonomous transport robotics that continuously learn from their surroundings, reducing manual labor and increasing efficiency.

Company size
11-50
Company stage
Series A
Total funding
$150M
Headquarters
Cambridge, MA
Founded
2025

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