Member of Technical Staff: Autonomy Infrastructure & Deployment

walden-robotics; Cambridge (MA); On-site; USD 120,000 - 170,000; Full time

Benefits offered by this job

Company equity

Annual cash bonus

401(k) with company match

Flexible PTO

Daily lunch

Job summary

Walden Robotics is building a team to advance general-purpose robot deployment and inference. You will own substantial parts of the deployment and live fleet serving stack, evaluate policies on real robots, and drive model improvements through data-driven analyses.

The role emphasizes end-to-end ownership, production-quality software, and collaboration with policy and infra teams to move models from training into deployment.

Qualifications

• Hands-on experience shipping production services or systems.
• Ability to own components independently and operate them reliably in production.
• Comfort debugging across the stack from model behavior to hardware limits.
• Comfort reasoning about policy behavior using data and metrics.

Responsibilities

• Deploy and own components of the deployment and on-robot inference pipeline.
• Design and run closed-loop evaluations of policies on real robots.
• Diagnose policy failures and fix them via fine-tuning, data, or model changes.
• Build telemetry, logging, and KPI-tracking for field performance.

Skills

Software engineering

Ownership

Full-stack debugging

Data-driven reasoning

Operational mindset

ML fundamentals

Job description

At Walden Robotics, we envision a world where general-purpose robots dramatically improve the quality of life for all people—supporting us at home, at work, in factories, on farms, and beyond. To accomplish this, we are building a team of exceptional professionals who combine world-class technical skills with creative vision, grounded in humility and collaboration.

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 [e-mail].

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.