Senior AI/ML Engineer (Autonomous Vehicles)

Location: Sunnyvale, California; hybrid, with on-site attendance at least 3 times a week; travel required less than 25%.

Base compensation: $170,600–$261,300 annually, plus incentive compensation and benefits.

Company

hirify.global develops automotive technologies, including autonomous vehicle systems focused on safer and more efficient transportation.

What you will do

• Validate optimized model implementations against reference implementations by defining operation-level equivalence criteria and adversarial test inputs.
• Connect numerical differences from quantization, compilation, and precision reduction to open-loop driving metrics and closed-loop vehicle behavior.
• Build Jacobian- and Hessian-based sensitivity, robustness, and out-of-distribution analysis tooling for repeated checkpoint evaluation.
• Design diagnostics to detect and root-cause training instabilities, including gradient vanishing and explosion, loss spikes, silent divergence, and gradient/update trajectory behavior under schedules such as WSD.
• Optimize metric computation, gradient decomposition, and diagnostic logging for distributed training without significant throughput cost or out-of-memory risk.

Requirements

• Working command of numerical analysis and matrix theory, including Jacobian/Hessian estimation, spectral properties, conditioning, and floating-point error analysis.
• Strong understanding of neural network training, including loss landscapes, gradient and error propagation, optimizer dynamics, and training failure modes.
• Adversarial approach to numerical edge cases, including denormals, extreme dynamic range, catastrophic cancellation, degenerate shapes, and accumulation-order effects.
• High proficiency in PyTorch and Python, with experience building mathematically defensible analytical tools that run efficiently in production training and evaluation loops.
• Ability to turn numerical analysis into actionable engineering decisions and trace anomalous results back to their originating operations.
• Bachelor's, Master's, or PhD in Applied Mathematics, Control, Physics, Computer Science, Data Science, or a closely related quantitative field.

Nice to have

• Hands-on experience debugging large-scale training runs, including loss spikes, numerical divergence, and training dynamics.
• Experience with distributed training beyond DDP, including FSDP, Megatron-LM, DeepSpeed, or 3D parallelism, especially low-overhead observability over sharded parameters and gradients.
• Experience with quantization, compiler toolchains, or inference-time numerical parity.
• Experience building or scaling evaluation pipelines and metrics for autonomous driving or ADAS systems.
• Published or applied work in model robustness, out-of-distribution generalization, or adversarial and perturbation analysis.

Culture & Benefits

• Hybrid work arrangement with required reporting to the Sunnyvale location at least 3 times per week.
• Relocation benefits are available for candidates who qualify under company policy.
• Health, dental, vision, Health Savings Account, and Flexible Spending Account options.
• Retirement savings plan, sickness and accident benefits, life insurance, paid vacation, and paid holidays.
• Incentive pay based on company performance, job level, and individual performance.

Hiring process

• Applicants may be required to complete a role-related assessment and/or pre-employment screening.