Staff Autonomy Engineer

Brain Corp•San Diego, CA

17h•$209,644 - $262,055

About The Position

As a Staff Autonomy Engineer on the R&D team, you will be a hands-on technical leader setting direction for the AI that powers Brain Corp’s robots. You will architect systems spanning perception, mapping, prediction, and planning; lead multi-quarter initiatives from research through fleet rollout; and raise technical standards across the organization. The role works across team boundaries with engineering managers, product, and hardware on decisions that shape the roadmap, while providing technical guidance through reviews, mentorship, and personal example. Staff engineers are expected to work across the modern autonomy stack, including foundation models, learned policies, classical estimation and planning, and the production systems that integrate them. Brain Corp is a San Diego-based AI company developing autonomous technology for robotics. Its BrainOS Robotics Platform powers a fleet of more than 50,000 autonomous mobile robots used in commercial public spaces, including floor cleaning and inventory management. The company is funded by the SoftBank Vision Fund, Clearbridge, and Qualcomm Ventures.

Requirements

Master’s degree or Ph.D. in Computer Science, Robotics, Electrical Engineering, or a related field, or equivalent demonstrated experience.
At least 7 years of industry experience in embodied AI, such as robotics, self-driving, or drones, including some time in a senior or lead capacity.
Deep expertise in at least two of machine learning, SLAM and state estimation, robotic perception, and motion planning.
Strong fluency in C++ and Python in a Linux environment.
Demonstrated experience taking research ideas into production at meaningful scale.
Hands-on expertise with PyTorch and/or JAX, modern training infrastructure, and contemporary architectures such as transformers, diffusion models, and VLA/foundation models.
Extensive experience designing robotic systems with ROS 2 or comparable middleware and modern simulation environments such as Isaac Sim, MuJoCo, or Gazebo.
Strong record of shipping ML-driven features to production hardware, including monitoring, debugging failure modes, rollback, and on-call responsibilities.
Strong systems and architectural judgment, with the ability to defend a position and update it when evidence changes.
Knowledge of modern software engineering fundamentals, including CI/CD, code review culture, observability, and iterative delivery.
Excellent written and verbal communication skills, including writing design documents, giving talks, and producing readable code.

Nice To Haves

Experience with on-device acceleration, such as TensorRT, ONNX, or custom CUDA kernels.
Experience with large-scale data infrastructure.
Experience leading distributed teams.
Research impact demonstrated through publications at venues such as CoRL, RSS, ICRA, NeurIPS, CVPR, or ICML, or through open-source equivalents, and a record of converting research into products.

Responsibilities

Set technical direction and own multi-quarter roadmaps for major areas of the autonomy stack, such as perception, SLAM, prediction, planning, and underlying ML systems.
Lead large, ambiguous projects end-to-end, from problem definition and approach selection through execution across engineers and fleet deployment.
Make and defend technical decisions about building versus borrowing, learned versus classical methods, and depth versus breadth.
Partner with engineering managers, product, and hardware on staffing, sequencing, and risk, and serve as a senior technical voice in cross-functional planning.
Represent the team externally through activities such as publications, conferences, recruiting, and customer technical discussions.
Architect and contribute to systems spanning learned perception, neural and classical SLAM, behavior prediction, and motion planning.
Drive adoption of learning approaches such as imitation learning, diffusion policies, vision-language-action models, and reinforcement learning where appropriate, integrating them with production classical components.
Build and improve data engines and evaluation infrastructure that turn fleet logs into training data, regression suites, and deployed improvements.
Lead embedded-compute performance and deployment work, including model optimization, quantization, distillation, and runtime engineering on GPU or accelerator hardware.
Monitor relevant research literature and translate useful findings into the codebase.
Mentor senior and mid-level engineers through design reviews, code reviews, and technical standards.
Lead hiring activities, including shaping interview loops, calibrating decisions, closing candidates, and onboarding new hires.
Champion testing, observability, documentation, simulation infrastructure, and developer tooling to help the organization deliver effectively.

Benefits

Discretionary annual target bonus.
Stock options.
401(k) plan with match, no waiting period, and immediate vesting.
Medical plan options, including an HSA with employer contribution.
Dental insurance.
Vision insurance.
Life and disability insurance.
Employee Assistance Program.
Legal and identity support plans.
Pet insurance.
Flexible Spending Accounts for medical and dependent care.
Flexible vacation and paid sick leave.
Paid time off for volunteering in the community.
10 paid company holidays and a winter company shutdown.
Daily on-site lunch available in the San Diego office.
On-campus gym with pool and tennis courts in the San Diego office.
Colleague events, including monthly game nights, hikes, wellness challenges, and community events.
Internal continuous learning events.
Opportunities to share personal interests and hobbies with the company.