Staff / Senior Machine Learning Engineer (Reinforcement Learning)

Location: Hybrid role based in the office in Sunnyvale, California, USA

Salary: $311,850–$389,400 per year, plus equity.

Company

hirify.global develops end-to-end autonomous driving systems and machine learning technology for safe, driverless operation.

What you will do

• Shape 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 diverse driving data.
• Evaluate policies through offline metrics, open-loop tests, closed-loop simulation, and on-road testing.
• Productionize successful methods, lead technical reviews, mentor engineers, and communicate technical decisions.

Requirements

• Strong experience developing and experimentally validating reinforcement learning or sequential decision-making methods for complex, high-dimensional problems.
• Deep knowledge 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.
• Strong experimental judgment and senior-level ownership across research and engineering teams.
• Ability to work in a hybrid arrangement based in the Sunnyvale office.

Nice to have

• Experience with offline reinforcement learning, imitation learning, reward modeling, preference learning, or post-training large neural policies.
• Background 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

• Full-time employment with a hybrid working policy combining office and home working.
• Time in offices and workshops supports collaboration, innovation, learning, and technical relationships.
• Competitive equity package in addition to the stated salary range.