Member of Technical Staff Autonomy Infrastructure & Deployment

You will build the infrastructure and inference-time algorithms that get policies onto robots and keep them running well. As an MTS you own substantial pieces of the deployment and serving stack ship them to a live fleet and work on the models themselves: evaluating them on real hardware diagnosing failures and turning findings into model improvements.

Core Responsibilities:

• Deployment Components: Implement and own components of the deployment and on-robot inference pipeline.
• Model Evaluation: Design and run closed-loop evaluations of policies on real robots and own the picture of what each model can and cannot do.
• Model Improvement: Diagnose policy failure modes from field data and fix them through fine-tuning data or model changes working with the pretraining team.
• Field Evaluation & KPIs: Build telemetry logging and KPI-tracking that surface how policies behave in the field and use that data to attribute failures to the model the data or the system.
• Reliability & Performance: Improve reliability and performance of real-time inference and rollout tooling.
• Debugging: Debug issues spanning application code models and hardware often under production urgency.
• Collaboration: Partner with policy and infra teams to move models from training into deployment and bring field findings back into training and evaluation.
• Inference-Time Algorithms: How the policy runs on the robot including action chunking and execution real-time and asynchronous inference and model optimizations such as quantization and distillation that trade latency against policy quality.

Required Qualifications:

• Software Engineering: Strong fundamentals and experience shipping production services or systems.
• Ownership: The ability to own components independently and operate them reliably in production.
• Full-Stack Debugging: Comfort debugging across the stack from model behavior and application logic down to hardware limits.
• Data-Driven Reasoning: Comfort reasoning about policy behavior from data and metrics rather than intuition alone.
• Operational Mindset: A pragmatic ownership-minded approach to on-call and operational work.
• ML Fundamentals: Hands-on experience training and evaluating deep models with strong engineering fundamentals in a modern ML framework.

Preferred Qualifications:

• Experience with edge/embedded deployment real-time systems or ML inference.
• Familiarity with observability and telemetry tooling.
• Exposure to robotics control imitation learning or VLA models.

Walden Robotics offers a competitive total compensation program including salary annual cash bonus company equity company-subsidized insurance programs 401(k) with company match flexible PTO daily lunch and other benefits. The pay ranges noted on our posts are for salary only.

Walden Robotics is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age ancestry color family or medical care leave gender identity or expression genetic information marital status medical condition national origin physical or mental disability political affiliation protected veteran status race religion sex (including pregnancy) sexual orientation or any other characteristic protected by applicable laws regulations and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application or the recruiting process please send a request to .

Walden Robotics participates in E-Verify. If you receive an offer of employment from Walden you will need to go through the E-Verify process of digital verification of your employment authorization documents as provided on the Form I-9. Participation in E-Verify does not limit your right to work and verification will only be completed after you become an employee with Walden Robotics.