Autonomy Infra Engineer - Real-Time Robotics Deployment

Walden Robotics; Cambridge (MA); On-site; USD 120,000 - 180,000; Full time

Benefits offered by this job

401(k) with company match

Company subsidized insurance

Flexible PTO

Daily lunch

Job summary

Walden Robotics in Cambridge, MA is seeking an MTS to build the deployment and serving stack for policies on robots. You will own substantial pieces of the deployment and ship them to a live fleet, ensuring smooth operation of real-time inference.

Responsibilities include implementing deployment components, building telemetry and KPI surfaces, improving reliability and performance, and collaborating with policy and infra teams to move models from training to production.

Qualifications

• 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 application logic down to hardware limits.
• Operational Mindset: A pragmatic, ownership-minded approach to on-call and operational work.

Responsibilities

• Deployment Components: Implement and own components of the deployment and on-robot inference pipeline.
• Telemetry & KPIs: Build telemetry, logging, and KPI-tracking that surface how policies behave in the field.
• 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.

Job description

Walden Robotics in Cambridge, MA is seeking an MTS to build the deployment and serving stack for policies on robots. You will own substantial pieces of the deployment and ship them to a live fleet, ensuring smooth operation of real-time inference.

Responsibilities include implementing deployment components, building telemetry and KPI surfaces, improving reliability and performance, and collaborating with policy and infra teams to move models from training to production.