Jobs at Menlo Inc.

Design, integrate, and bring up electronic systems for a humanoid robot: schematics and PCB layouts for power/motor control, wiring harness and enclosure integration, system bring-up and debugging, test protocol definition and failure analysis, collaborate with firmware/controls, and contribute open-source manufacturing documentation.

Open invitation for unconventional, hybrid thinkers to join Menlo Research's applied R&D lab building an open-source humanoid robot platform. Contributors work across hardware, locomotion, autonomy, simulation, and software infrastructure, shipping to real robots, using AI as a collaborator, and thriving in fast-moving, high-performance teams.

Design, analyze, and validate mechanical and kinematic systems for a humanoid robot. Produce CAD models and production drawings, run FEA and physical tests, perform DFM with contract manufacturers, set up instrumented test rigs, and coordinate cross-discipline integration from prototype to production. Contribute to open-source hardware releases and manufacturing documentation.

Develop and deploy real-time navigation for bipedal humanoid robots, owning localization, mapping, SLAM, and planning pipelines. Integrate navigation with perception and locomotion, iterate sim-to-real using Uranus and hardware, build evaluation infrastructure, and contribute open-source tooling.

Work end-to-end on real customer deployments of humanoid robots: diagnose field failures, collect and curate deployment data, design evaluations, fine-tune models, and turn one-off fixes into reusable research and product improvements while collaborating with core research and platform teams.

Architect and scale distributed infrastructure for fleets of humanoid robots, build low-latency ingestion and streaming pipelines, develop large-scale training and inference platforms, support hardware-in-the-loop simulation and continuous learning, and lead observability, storage, consensus, and GPU orchestration decisions.

Volunteer community representative who moderates Discord/forums, amplifies Menlo announcements, hosts or helps run local events and workshops, previews tools and APIs, creates tutorials/content, and provides early feedback to grow Menlo's AI and robotics community.

Work with founders on fundraising, investor relations, partnerships, and M&A. Drive end-to-end fundraising processes, source and close commercial pilots and strategic deals, run financial modeling and diligence, and represent Menlo externally. Shape partnership offerings with product teams and synthesize market intelligence to inform strategic investments and acquisitions.

Develop, train, and deploy dexterous manipulation policies for a humanoid robot. Build grasp planning and contact-rich control pipelines, run teleoperation and data-collection infrastructure, integrate manipulation with perception and autonomy, diagnose hardware failures, and contribute open-source tooling. Work spans model-based control, imitation and reinforcement learning, with a focus on real-world robot deployment.

Own and evolve Menlo's hybrid cloud and on-prem platform: operate Kubernetes clusters, CI/CD/GitOps, observability, inference and training infrastructure, core platform services, identity/SSO, networking, and reliability/cost planning at scale.

Design, train, and deploy real-time perception systems (detection, segmentation, depth, 3D reconstruction) for a humanoid robot. Fuse RGB, depth, and inertial data, optimize inference for embedded hardware, evaluate on physical robots, and collaborate with navigation and manipulation teams. Contribute to open-source releases.

Paid, hands-on fellowship where interns own end-to-end projects in software, systems, infrastructure, applied AI, or research. Work includes shipping production-grade features, debugging across codebases, writing documentation and runbooks, and collaborating with mentors. Minimum three-month on-site placements in Menlo offices; strong ownership, practical debugging, and engineering fundamentals required.

Develop and deploy locomotion controllers and learned policies for a bipedal humanoid. Train in simulation (Uranus), design rewards and curricula, close sim-to-real gaps, integrate with autonomy stack, analyze hardware telemetry, and contribute to open-source locomotion tooling.