Jobs at General Motors

Build and own scalable hybrid human/machine data-labeling platform for autonomous vehicles. Define roadmap, design APIs/UIs, integrate ML-driven annotation, improve data quality, and ship full-stack features across frontend, backend, and ML services.

Develop data-centric machine learning systems for autonomous driving, including dataset curation, sampling, mining, evaluation, foundation-model training, and scaling-law studies. Train models across large multi-GPU and multi-node datasets using real and synthetic driving data. Investigate failures, improve data quality, define predictive offline metrics, and collaborate on deploying models into onboard driving systems. The role applies self-supervised learning, imitation learning, reinforcement learning, and foundation-model fine-tuning to perception, trajectory generation, and driving behavior.

Leads software release program management for complex vehicle software. Builds and develops a team of technical program managers, establishes release standards, improves release frequency and predictability, manages risks and dependencies, coordinates validation and OTA delivery, and strengthens readiness, quality, governance, and post-release improvement processes. Partners with engineering, product, quality, security, configuration management, and senior leadership.

Lead the technical strategy for validating autonomous vehicle behavior across requirements, simulation, road data, closed-course testing, and fleet monitoring. Define release-readiness criteria, safety evidence, metrics, coverage, and traceability. Diagnose issues across perception, prediction, planning, and controls; guide validation tools and data pipelines; support launch decisions; and lead cross-functional reviews while mentoring engineers.

Leads engineers and data scientists evaluating autonomous-driving subsystems. Owns roadmap, staffing, metrics, statistical validation, regression detection, root-cause analysis, and release recommendations. Develops measures linking perception, planning, prediction, and localization errors to driving outcomes across simulation and road data. Applies VLMs and LLMs where useful, communicates findings to technical leaders, and builds trusted partnerships with autonomy teams. Requires substantial autonomy or ML evaluation experience, people management, strong statistics, Python, SQL, and C++ code-reading skills.

Leads four autonomy evaluation teams through managers and technical leads, owning strategy, roadmap, staffing, execution, and evaluation quality. Builds trusted metrics, classifiers, regression detection, and analysis processes that guide engineering priorities and software release decisions. Partners with autonomy, simulation, safety, and validation organizations, while hiring and developing technical leaders. Applies statistical analysis, Python, SQL, and emerging VLM and LLM methods to evaluate autonomous driving systems.

Develop and scale high-fidelity hardware-in-the-loop platforms and integration services for autonomous vehicle software validation. Build reliable, observable, and scalable software; support requirements generation, continuous integration, automated testing, issue tracking, and code reviews. Collaborate across AV software, AI, middleware, operating systems, sensor applications, and vehicle integration teams. Optimize software for dedicated hardware and deliver complex projects through incremental development.

Build and own physics-grade robotics simulation infrastructure for manipulation, perception, SLAM, and navigation validation. Model robot dynamics, sensors, scenes, and facilities; integrate simulation into CI; support AI data generation and policy evaluation; and lead virtual commissioning and digital twin capabilities. The role also validates simulation against real robot behavior, documents fidelity limitations, and supports internal robotics and test teams.

Own the strategy, roadmap, and delivery of GM’s unified customer authentication across web, mobile, and vehicle experiences. Lead adoption of passwordless authentication, passkeys, biometrics, mobile-to-vehicle handoff, persistent authentication, and risk-based controls. Partner with engineering, cybersecurity, privacy, legal, design, and vehicle teams to ensure secure, compliant experiences. Define OKRs, KPIs, and SLOs while influencing senior leadership and managing complex dependencies and launches.

Develop 2D and 3D simulation platforms for autonomous mobile robots, create realistic scenarios, integrate navigation software, debug simulated behavior, analyze results, and build regression testing workflows. Collaborate with robotics and AI teams to validate system performance and improve simulation capabilities. The hybrid role requires reporting to a designated location at least three times weekly and travel under 25%.

Leads the AV Data Processing and Services team in designing, building, and operating petabyte-scale distributed data pipelines, storage systems, batch and streaming services, and cloud infrastructure. Responsibilities include hands-on technical delivery, team hiring and mentorship, architecture and roadmap ownership, cloud cost optimization, reliability engineering, observability, incident management, and root-cause analysis.

Leads the Planner Evaluation engineering team for GM’s autonomous driving platform. Owns the technical vision and roadmap for simulation and on-road evaluation, guides architecture and implementation, establishes validation strategies, investigates system issues, defines metrics and test plans, and mentors engineers. The role requires production Python, C++ debugging, large-scale data analysis, cross-functional technical leadership, and experience evaluating dynamic or autonomous systems.

Own numerical analysis and observability for compressed and optimized AI models used in autonomous vehicles. Develop parity validation, sensitivity and robustness tooling, connect numerical differences to driving behavior, diagnose training instabilities, and build efficient diagnostics for distributed training. The role requires strong numerical analysis, neural network training, PyTorch, Python, and production engineering judgment.

Leads enterprise-scale engineering platforms for embedded software, simulation, and cloud workloads. Designs CI/CD, GitOps, Kubernetes, Terraform, release, quality-gate, progressive delivery, security, compliance, and observability architectures. Drives engineering productivity, standards, process improvements, and platform adoption across teams while mentoring DevOps and simulation developers. Requires deep real-time systems, safety-critical product, cloud, Kubernetes, Terraform, and embedded or simulation workflow expertise.

Design, build, and operate full‑stack data labeling tools and pipelines for autonomous vehicle ML. Implement scalable UIs and services, integrate ML-driven pre‑labeling/autolabeling, build automation and QA tooling, own projects end‑to‑end, and collaborate across ML, product, and ops to improve data quality and labeling velocity.

Design, implement, benchmark, and optimize CUDA-based GPU kernels and custom operators for on-vehicle ML inference. Build profiling/debugging tooling, integrate kernels into the ML runtime, collaborate with cross-functional teams, and deliver production-grade high-performance libraries meeting strict latency, throughput, and reliability targets.

Staff software engineer responsible for designing, building, and operating production authorization services. Leads access-control architecture, policy-driven authorization, security reviews, cloud-native microservices, secure APIs, observability, data modeling, deployment automation, and production support. Defines technical standards, mentors engineers, guides security practices, and partners across teams to deliver scalable, reliable authentication and authorization capabilities.

Design, develop, deploy, and operate large-scale data platform services supporting autonomous vehicle model training, mining, labeling, and replay. Define SLOs and SLIs, lead incident response, optimize cost and performance, and build self-service capabilities with ML and data engineering stakeholders. Own end-to-end technical projects, contribute to roadmap decisions, participate in reviews and planning, and mentor engineers.

Leads architecture and implementation of DevOps, CI/CD, build, release, developer productivity, and observability platforms for next-generation infotainment software. Designs scalable embedded Linux and Yocto pipelines, automates releases, builds self-service tooling, manages artifacts and quality gates, and improves operational reliability. Partners across engineering, infrastructure, quality, release management, and programs while mentoring engineers and influencing technical direction across teams.

Defines the vision, strategy, and roadmap for in-vehicle privacy, identity, driver recognition, authentication, and personalized experiences. Leads privacy controls, consent, digital keys, profiles, PINs, biometrics, and secure vehicle access across vehicle, mobile, cloud, account, and partner platforms. Coordinates cross-functional teams, manages product execution and launches, establishes success metrics, and drives continuous improvement while aligning customer needs, privacy requirements, security considerations, and technical constraints.