Senior Machine Learning Engineer

Role: Lead Machine Learning Engineer — Physical AI | Robotics | Autonomous Systems

Location: Austin, TX

Salary: $200,000 + Early Stage Equity

We’re working with a rapidly growing, well-funded startup building Physical AI systems for robotics and autonomous platforms. Backed by globally recognized investors, the team is developing ML-driven intelligence that allows robots to perceive, reason, and act in complex, real-world environments — far beyond the lab.

They are now hiring a Lead Machine Learning Engineer to own and scale core Physical AI capabilities across perception, decision-making, and autonomy, with models deployed directly onto robotic platforms and edge hardware. This is a senior, hands-on leadership role with significant influence over system architecture, ML strategy, and technical direction.

What You’ll Do

• Lead the design and deployment of Physical AI systems for robotic and autonomous platforms
• Own end-to-end ML pipelines: data strategy, training, evaluation, deployment, and monitoring in real-world environments
• Build and scale models for perception, prediction, and control (e.g. vision, multimodal, learning-based autonomy)
• Architect ML systems that operate reliably under real-world constraints (latency, noise, limited compute, sensor variability)
• Deploy and optimize models on edge hardware (e.g. NVIDIA Jetson) for real-time inference
• Define and track performance metrics across accuracy, robustness, safety, and latency
• Close the sim-to-real gap through field testing, on-robot evaluation, and continuous data collection
• Collaborate closely with robotics, systems, and hardware teams to integrate ML into full autonomy stacks
• Provide technical leadership and mentorship to other ML engineers
• Stay current with advances in Physical AI, robotics, and autonomous systems research
• Degree in Computer Science, Machine Learning, AI, Robotics, or related field
• Strong experience owning and shipping ML systems into real-world or robotic environments
• Solid grounding in ML fundamentals and modern deep learning architectures
• Expert-level Python for ML systems and experimentation
• Experience deploying models to production and edge environments
• Familiarity with real-time, safety-critical, or autonomy-focused systems
• Ability to lead technically while remaining hands-on
• Willingness to participate in field testing and on-robot validation

Bonus Experience (Nice to Have)

• Experience with robotics autonomy stacks or Physical AI systems
• Computer vision, perception, or multimodal ML experience
• Edge AI optimization on ARM or NVIDIA Jetson
• Simulation tools and sim-to-real workflows
• Experience working on autonomous, aerospace, or defense-adjacent systems

Package

• Strong PTO
• 401(k)
• Life Insurance
• Short- & Long-Term Disability, Wellness Programs