Distributed Systems Engineer

Menlo Research is an Applied R&D lab building Asimov, an open-source humanoid robot platform, and the full software stack that powers it. Our mission is to make humanoid labor economically viable, turning software into physical labor at scale. We build across the full stack: hardware architecture, locomotion, autonomy, simulation, and infrastructure. We move fast, ship to real robots, and open-source everything we can. If you want your work to matter beyond a paper or a demo, this is the place.

The Role

We are looking for a Distributed Systems Engineer to architect and scale the infrastructure that powers fleets of humanoid robots operating across the world. You will work across the full stack of robotics infrastructure, from low-latency streaming and cloud simulation to large-scale training and telemetry pipelines. You will work directly with the founders and technical leadership to design the systems that let hundreds of robots learn, share, and act as one.

What You Will Do

• Architect and scale distributed systems that handle petabytes of sensory, telemetry, and control data across cloud and edge environments
• Design data ingestion and streaming pipelines connecting fleets of robots to the cloud in real time (video, LiDAR, joint states, audio)
• Build large-scale training and inference platforms for multimodal foundation models powering robot autonomy and teleoperation
• Collaborate with ML and Robotics engineers to support hardware-in-the-loop simulation, policy rollout, and continuous learning
• Develop internal observability systems for fleet monitoring, reliability, and performance tuning
• Lead infrastructure decisions, from distributed storage and consensus protocols to GPU orchestration and network reliability

• 7+ years of professional software engineering experience, with deep expertise in distributed systems, networking, or data infrastructure
• Proven ability to build and operate production-grade distributed systems handling massive scale and mission-critical workloads
• Proficiency in Go, Rust, C++, or Python, with strong fundamentals in concurrency, networking, and systems performance
• Experience with cloud-native architectures (Kubernetes, gRPC, Kafka, S3, Ray, or similar frameworks)
• Strong understanding of data consistency, replication, and fault tolerance across heterogeneous environments
• Experience with GPU-based workloads, model training, or edge compute orchestration is a strong plus
• Excellent analytical skills and a bias toward building fast, measurable, and reliable systems

• Experience building distributed training or large-scale simulation systems
• Familiarity with real-time robotics workloads, including streaming from physical sensors and actuators
• Prior work with telemetry, observability, or fleet-scale systems in production
• Contributions to open-source infrastructure, AI frameworks, or robotics middleware (ROS, gRPC, Mediasoup, etc.)

You will be part of a tight-knit team defining the next generation of humanoid robots. With genuine ownership of system architecture and the freedom to innovate, you will see your designs come to life in real-world deployments. If you thrive in fast-paced, open, collaborative environments, let's build the future of robotics together.

A Note on AI

You don't need deep AI expertise for every role, but we do expect everyone at Menlo to be intellectually curious, drawn to tinkering and discovery, and excited to use AI as a real collaborator in their work. For some roles, AI fluency is a core requirement. When that's the case, we'll say so explicitly in the qualifications. People who thrive here don't treat AI as a novelty. They use it to think better, and make their work easier for others to build on.

Equal Opportunity and Accommodations

We hire talented people from a wide range of backgrounds. If you're excited about a role but don't meet every bullet, we still encourage you to apply. Menlo Research is an equal opportunity employer and does not discriminate on the basis of any legally protected characteristic. Menlo provides reasonable accommodations during the application process. If you need one, please let your recruiter know.