Senior / Staff Machine Learning Engineer (Autonomous Vehicles)

Location: Sunnyvale, California, USA; hybrid working model with in-person collaboration and remote work. The role may involve hands-on work in vehicle workshops and labs.

Company

hirify.global is building an AI platform for autonomous driving that enables vehicles to learn from real-world experience and operate across complex urban environments.

What you will do

• Own and execute the reinforcement learning roadmap for Driving Core and Core Model Safety.
• Develop offline, off-policy, and reward-guided optimization methods beyond behavior cloning.
• Improve reward models and learning signals used to train and evaluate driving policies.
• Build large-scale training and experimentation workflows using driving data.
• Evaluate policies through offline metrics, open-loop tests, closed-loop simulation, and on-road testing.
• Productionize successful methods, communicate technical decisions, review designs and code, and mentor engineers.

Requirements

• Strong track record developing and experimentally validating reinforcement learning or sequential decision-making methods on complex, high-dimensional problems.
• Deep understanding of policy and value learning, off-policy learning, function approximation, distribution shift, and learned-objective failure modes.
• Hands-on experience with behavior cloning, reinforcement learning, or related methods.
• Proficiency in Python and PyTorch, with experience building reliable machine learning training and evaluation systems.
• Excellent experimental judgment and the ability to define hypotheses, metrics, ablations, and technical decisions.
• Senior-level ownership, cross-functional collaboration, and clear written and verbal communication.

Nice to have

• Experience with offline reinforcement learning, imitation learning, reward modeling, preference learning, or post-training large neural policies.
• Experience in autonomous vehicles, robotics, control, motion planning, vehicle dynamics, or collision avoidance.
• Experience with closed-loop simulation, off-policy evaluation, uncertainty or calibration, and rare or shifted conditions.
• Experience training multimodal, transformer-based, or generative policy models at scale.
• Proficiency in C++, CUDA, distributed training, or production machine learning performance optimization.

Culture & Benefits

• Hybrid work combining office collaboration with focused remote work.
• Relocation support and visa sponsorship where applicable.
• Market-benchmarked salaries and meaningful equity.
• Learning and development budgets for training, conferences, and growth.
• Health, dental, parental leave, retirement or pension benefits where applicable, therapy access, wellbeing partnerships, and team socials.

Hiring process

• Initial recruiter call lasting 30 minutes.
• Competency interviews, including hiring manager and applied machine learning interviews, lasting two hours in total.
• Deep-dive technical interviews covering programming, systems design, PyTorch debugging, and technical leadership, followed by a final mission and values interview.