Remote Automotive Controller Jobs in Dallas, OR (NOW HIRING)

Agility Robotics builds Digit, the world's first commercially deployed humanoid robot — already at work in real warehouses and manufacturing facilities today. Digit has proven itself in structured work cells, and now we're pushing beyond them: into open floors, shared aisles, and dynamic spaces where people, forklifts, and other robots are all moving at once. That shift raises the bar for perception dramatically — Digit needs to reliably understand and react to an environment that's no longer predictable or controlled.

That means perception isn't just about capability — it's about trust. Every person Digit works near is counting on our perception systems to see them, understand them, and never get it wrong, whether Digit is in a fixed work cell or navigating a busy, ever-changing facility. We're looking for a Senior Staff AI Software Engineer, Perception to own the technical vision and system architecture for how Digit perceives and stays safe as we take on these more dynamic, less constrained environments.

• Own the technical vision and system architecture for Perception that enables safety and efficiency, defining how Digit will perceive and reason about humans and hazards as it expands from structured work cells into open, dynamic environments
• Analyze key architectural tradeoffs across sensing modalities, model design, redundancy, and compute budget, balancing safety, latency, and real-world robustness
• Stay hands-on with development to prototype, validate, or build critical pieces yourself
• Partner with functional safety and systems engineering to define the safety case strategy, identifying what architecture and evidence will be required
• Support failure mode and hazard analysis at the system level, shaping which risks the architecture must design around
• Evaluate emerging research and technology, and decide what's worth adopting into future robot architectures
• Set standards for verification and validation of ML-based perception systems, especially for rare and safety-critical scenarios
• Collaborate across navigation, controls, and hardware to make sure the perception architecture serves the whole robot
• Act as a senior technical mentor and partner to engineers and technical leads across the organization, and represent the architecture to executive leadership, partners, and customers as needed

• 8+ years in machine learning/perception for robotics, with a track record of defining system architecture
• Master's or Ph.D. in Artificial Intelligence, Robotics, Computer Science, or a related field, with a strong foundation in machine learning, robotics, and intelligent systems
• Demonstrated experience architecting ML/perception systems for safety-critical or safety-rated contexts — human detection, functional safety, automotive or robotics or a comparable regulated domain
• Deep expertise in deep convolutional neural networks, multi-object tracking, data association, supervised learning, and pose estimation, with strong intuition for where these approaches break down in unpredictable, real-world conditions
• Experience making system-level tradeoffs across sensing modalities, redundancy, and compute constraints on embedded/real-time hardware
• A track record of setting technical direction at an organization level
• Publications in your field are a plus (CVPR, ICCV, RSS, ICRA preferred)

• Direct experience supporting a safety case or certification submission
• Experience architecting redundant or diverse sensing systems for safety-rated perception
• Experience architecting perception systems for humanoid or legged robots specifically
• Experience representing technical architecture to regulators, third-party certifiers, or safety auditors
• Substantial experience with failure mode analysis (FMEA), hazard analysis, and safety case strategy for autonomous or robotic systems

Location

This is a remote role with the option to work hybrid if a commutable distance from our Salem, OR, Pittsburgh, PA, or Fremont, CA offices.

The final salary offered to a successful candidate will be dependent on several factors that may include but are not limited to: market location, job-related knowledge, skills, and experience. This range may change based on geographical location and may be modified in the future.