Senior AI/ML Engineer
General Motors1 day ago
Base Salary
$171k - $261k/yr
Responsibilities
- Rigorously validate optimized implementations against reference implementations by defining equivalence criteria and designing adversarial inputs.
- Map numerical differences from quantization, compilation, and precision reduction to open-loop and closed-loop autonomous-driving behavior.
- Build Jacobian- and Hessian-based sensitivity and robustness analysis tooling for checkpoints and out-of-distribution inputs.
- Design diagnostics to detect and root-cause gradient vanishing, gradient explosion, loss spikes, silent divergence, and other training instabilities.
- Develop gradient and update trajectory decompositions into loss-descent and oscillatory components under modern training schedules such as WSD.
- Implement metric computation, gradient decomposition, and diagnostic logging within distributed training jobs with negligible throughput cost and no out-of-memory risk.
- Turn numerical analyses into actionable engineering decisions and trace anomalous results to their originating operations.
Requirements
- Bachelor's, Master's, or PhD in Applied Mathematics, Control, Physics, Computer Science, Data Science, or a closely related quantitative field.
- Strong 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, loss landscapes, gradient and error propagation, optimizer dynamics, and training failures.
- Ability to investigate numerical edge cases such as 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.
- Hands-on experience debugging large-scale training runs, including loss spikes and numerical divergence.
- Experience with distributed training beyond DDP, such as FSDP, Megatron-LM, DeepSpeed, or 3D parallelism.
- Experience with quantization, compiler toolchains, or inference-time numerical parity.
- Experience building or scaling evaluation pipelines and metric formulations for AV or ADAS systems.
- Published or applied work in model robustness, out-of-distribution generalization, or adversarial/perturbation analysis.
Benefits
- Health and wellbeing benefits including medical, dental, vision, Health Savings Account, and Flexible Spending Accounts.
- Retirement savings plan, sickness and accident benefits, life insurance, and paid vacation and holidays.
- Hybrid work arrangement requiring the selected candidate to report to a specific location at least three times per week or at a frequency set by the manager.
- Less than 25% travel is required.
- The role may be eligible for relocation benefits.
- Incentive bonus potential based on company performance, job level, and individual performance.
Categories
About General Motors
General Motors designs, manufactures, and sells cars, trucks, and electric vehicles for consumers and commercial fleets under brands including Chevrolet, GMC, Cadillac, and Buick. A public company on the NYSE headquartered in Detroit and founded in 1908, it operates globally and is developing EVs on its Ultium battery platform. Revenue comes from vehicle and parts sales, connected services such as OnStar, and financing through GM Financial.