Staff Machine Learning Engineer (Autonomous Vehicles)

Location: Sunnyvale, California, USA; hybrid work with regular office presence

Salary: $336,400–$370,300 per year, plus equity

Company

hirify.global develops embodied AI software and foundation models for mapless, hardware-agnostic automated driving systems.

What you will do

• Own Core Model Safety roadmap themes from research and experimentation through technology transfer.
• Train and deploy end-to-end AV 2.0 models across a global vehicle fleet using large-scale, diverse data.
• Build open-loop and closed-loop evaluations for core capabilities and representation learning.
• Improve model generalisation across vehicles, markets, and driving conditions.
• Work with AV Core, Evaluation, Product Engineering, fleet, simulation, and research partners on roadmaps and failure modes.
• Mentor engineers and lead cross-functional technical work without formal line management.

Requirements

• 5+ years of machine learning engineering experience, including scoping ambiguous problems, defining evaluations, and establishing technical direction.
• Proficiency in Python and relevant languages such as C++ and CUDA, with strong software engineering practices.
• Experience with PyTorch and transformer-based, multimodal architectures, including VLMs, VLAs, or equivalent.
• Hands-on experience training shared representations with multiple tasks or objectives and managing data and loss trade-offs.
• Staff-level technical leadership, research literacy, pragmatic decision-making, and cross-functional leadership.
• Willingness to work in a hybrid arrangement based in the Sunnyvale office.

Nice to have

• Experience in autonomous vehicles or robotics, including deployment and closed-loop validation on physical systems.
• Experience with 3D scene understanding, geometric and semantic perception, and large-scale semantic enrichment.
• Experience with reward modelling, behaviour modelling, model introspection, or interpretability.
• Experience with redundant or fallback architectures and safety-critical systems.
• Experience with foundation model pretraining, applied engineering, large-scale training infrastructure, or agentic workflows.

Culture & Benefits

• Hybrid working combines time in offices and workshops with time working from home.
• Access to large-scale training and fleet data.
• Competitive equity package.
• Inclusive interview experience with accommodations available on request.
• Inclusive and respectful working environment focused on diverse perspectives and continuous learning.