About Asimov
Asimov is our open‐source humanoid robotics platform, building every layer—from structural frames and electromechanical assemblies to power electronics, cabling, and embedded control hardware. We aim to democratize advanced robotics by sharing designs, firmware, and ML models with the global community.
The Role
We’re looking for a Distributed Systems Engineer to architect and scale the infrastructure that powers fleets of humanoid robots operating across the world.
This is a career-defining opportunity to work across the full stack of robotics infrastructure — from low-latency streaming and cloud simulation to large-scale training and telemetry pipelines.
You’ll 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’ll 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.
What You’ll Bring
- 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.
Bonus Points
- 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.).
Why Join Menlo?
You’ll 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’ll 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.
