Senior/Staff Machine Learning Engineer (Autonomous Vehicles)

Location: Sunnyvale, California, USA; hybrid working model with in-person collaboration in the office, vehicle workshops, and labs.

Salary: $311,850–$389,400 annually, plus equity and location-dependent benefits.

Company

hirify.global develops an end-to-end AI platform for autonomous driving that learns from real-world driving experience and supports scalable deployment across vehicle manufacturers.

What you will do

• Lead the technical direction and delivery of learned emergency manoeuvre prediction and collision detection models.
• Own Core Model Safety roadmap areas from problem definition and research through experiments, evaluation, integration, and deployment evidence.
• Train and deploy end-to-end autonomous driving models using large-scale fleet data across vehicles, markets, and driving conditions.
• Collaborate on online occupancy models for geometric and semantic perception.
• Build open-loop and closed-loop evaluations for core capabilities and representation learning.
• Partner with AV Core, Evaluation, Product Engineering, fleet, simulation, and product teams while mentoring engineers.

Requirements

• Strong track record in machine learning engineering, including scoping ambiguous problems, designing evaluations, and setting technical direction.
• Hands-on experience with machine learning systems deployed in the real world.
• Proficiency in Python and PyTorch, with strong software engineering practices and experience building reliable training and evaluation systems.
• Excellent experimental judgment, including hypothesis formation, metric design, disciplined ablations, and technical decision-making.
• Senior-level ownership, cross-functional collaboration, and clear written and verbal communication.

Nice to have

• Experience in autonomous vehicles or robotics, including physical-system deployment and closed-loop validation.
• Experience with 3D scene understanding, representation learning, geometric and semantic perception, or large-scale semantic enrichment.
• Experience evaluating rare events using simulation, fleet data, or heterogeneous real-world data.
• Experience with transformer-based, multimodal, vision-language, or vision-language-action models.
• Proficiency in C++, CUDA, distributed training, or production machine learning performance optimization.

Culture & Benefits

• Hybrid work combining office collaboration with focused remote work and core hours.
• Relocation support and visa sponsorship where applicable.
• Meaningful equity and annual market-benchmarked salaries.
• Learning and development budgets for training, conferences, and professional growth.
• Health, dental, retirement or pension, enhanced parental leave, therapy access, wellbeing partnerships, and team socials, depending on location.

Hiring process

• Recruiter screen: 30 minutes.
• Competency interviews with the hiring manager and applied ML interviewer: 2 hours total.
• Deep-dive technical interviews covering programming, systems design, PyTorch debugging, and technical leadership, followed by a final mission and values interview.