Senior AI/ML Engineer (Autonomous Vehicles)

Location: Sunnyvale, California, United States; hybrid with reporting to the location at least 3 times per week. Travel required up to 25%. The role may be eligible for relocation benefits.

Base compensation: $170,600–$261,300 per year, plus potential incentive pay.

Company

hirify.global develops automotive products and autonomous vehicle technologies with a vision of zero crashes, zero emissions, and zero congestion.

What you will do

• Validate optimized model implementations against reference implementations and design adversarial inputs that expose numerical equivalence violations.
• Connect numerical differences from quantization, compilation, and precision reduction to open-loop metrics and closed-loop autonomous driving behavior.
• Build Jacobian- and Hessian-based sensitivity, robustness, and out-of-distribution analysis tooling for repeated checkpoint evaluation.
• Design diagnostics for training instabilities, including gradient vanishing and explosion, loss spikes, silent divergence, and gradient or update trajectory decomposition.
• Optimize metric computation, diagnostic logging, and gradient analysis for distributed training without significant throughput or memory overhead.

Requirements

• Strong command of numerical analysis, matrix theory, spectral properties, conditioning, Jacobian and Hessian estimation, and floating-point error analysis.
• Deep understanding of neural network training, loss landscapes, gradient and error propagation, optimizer dynamics, and training failure modes.
• Ability to investigate numerical edge cases such as denormals, extreme dynamic range, catastrophic cancellation, degenerate shapes, and accumulation-order effects.
• High proficiency in Python and PyTorch, with experience building mathematically defensible analytical tools for production training and evaluation loops.
• Bachelor’s, master’s, or PhD in applied mathematics, control, physics, computer science, data science, or a related quantitative field.

Nice to have

• Experience debugging large-scale training runs and investigating numerical divergence or training dynamics.
• Experience with FSDP, Megatron-LM, DeepSpeed, 3D parallelism, or other distributed training systems beyond DDP.
• Experience with quantization, compiler toolchains, inference-time numerical parity, AV or ADAS evaluation, model robustness, OOD generalization, or perturbation analysis.

Culture & Benefits

• Hybrid work arrangement with regular onsite collaboration.
• Health, dental, and vision coverage, plus Health Savings Account and Flexible Spending Account options.
• Retirement savings plan, sickness and accident benefits, life insurance, paid vacation, and holidays.
• Opportunity to work on autonomous vehicle model deployment and safety-critical numerical validation.

Hiring process

• Role-related assessments and pre-employment screening may be required.