Jobs at GenomeKey

Leads software release program management for complex vehicle software. Builds and manages a team of technical program managers, establishes release standards, improves release frequency and predictability, manages risks and dependencies, coordinates validation and OTA delivery, strengthens quality governance, and communicates release readiness and trade-offs to senior stakeholders.

Design, build, and operate data‑labeling tools and pipelines for autonomous vehicle ML. Lead end‑to‑end platform projects, partner with ML/Prod/Data teams, apply ML to annotation, and deliver scalable full‑stack experiences improving labeling quality and velocity.

Develop data-centric machine learning solutions for autonomous driving. Responsibilities include designing dataset mixtures, scaling experiments, model training recipes, data curation, auto-labeling, deduplication, evaluation metrics, failure analysis, and large-scale multi-GPU training. The role applies self-supervised, imitation, and reinforcement learning to real and synthetic driving data while collaborating on production deployment and onboard vehicle systems.

Leads the technical strategy and systems engineering approach for autonomous vehicle behavior validation. Defines release readiness, safety evidence, requirements, metrics, coverage, and acceptance criteria across the validation lifecycle. Diagnoses issues across the autonomy stack, designs simulation and road-data validation strategies, guides scalable tools and data pipelines, and advises leadership on launch decisions. Requires deep autonomous driving expertise, strong Python and agentic workflow skills, systems engineering experience, and cross-functional technical leadership.

Build software tools, services, data pipelines, and machine-learning integrations that support large-scale data labeling for autonomous vehicle models. Develop frontend and backend features, automation, quality systems, dashboards, autolabeling workflows, and scalable APIs. Collaborate with machine-learning, data science, product, and operations teams while contributing to production systems and AI-assisted engineering workflows.

Leads four autonomy evaluation teams through managers and technical leads, owning strategy, roadmap, staffing, and execution. Oversees metrics, classifiers, statistical analysis, regression detection, and insights that guide software releases and engineering priorities. Builds trusted evaluation processes across road and simulation testing, introduces VLM and LLM methods, develops leaders, and communicates evidence-based recommendations to technical and executive stakeholders.

Lead the Subsystem Evaluation team for autonomous driving, setting its roadmap, staffing, and technical direction. Build and validate metrics that connect perception, planning, prediction, and localization errors to driving outcomes. Oversee regression detection, root-cause analysis, simulation and road-data evaluation, and statistically sound experimentation. Hire and develop engineers and data scientists while partnering with autonomy teams and leadership to communicate trusted findings and prioritize improvements.

Build and own physics-based robotics simulation infrastructure for manipulation, perception, SLAM, and navigation validation. Model robot dynamics, actuation, and sensors; develop scene-authoring and CAD import tools; integrate simulations into CI; support AI data generation and policy evaluation; and lead virtual commissioning and digital twin capabilities. The role also quantifies simulation-to-hardware gaps and supports robotics and test teams as an infrastructure owner.

Develop and scale high-fidelity hardware-in-the-loop validation platforms for autonomous vehicle software. Build reliable, observable, high-performance platform and integration services; support requirements generation, continuous integration, automated testing, issue tracking, and code reviews. Collaborate with AV software, AI, middleware, OS, sensor, and vehicle integration teams to deliver complex projects through incremental development.

Own the strategy, roadmap, and delivery of secure customer authentication across GM’s 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 meet security and privacy standards, define product KPIs, reduce authentication friction, and drive a unified GM identity ecosystem.

Develop and enhance 2D and 3D simulation platforms for autonomous mobile robots. Build realistic scenarios, integrate robot navigation software, debug simulated behavior, analyze results, and create regression and progression testing workflows. The role collaborates with robotics and AI teams to improve validation, system robustness, and simulation capabilities. Required expertise includes Python or C++, robotics simulation platforms, physics or game engines, ROS 2, and core robotics modules such as localization, mapping, and planning.

Lead the AV Data Processing and Services team as a player-coach, designing and delivering petabyte-scale distributed data pipelines, storage systems, batch and streaming services, and cloud infrastructure. Recruit and mentor engineers, own technical strategy and roadmaps, optimize compute and storage costs, and establish reliability, observability, SLAs, data lineage, testing, incident management, and root-cause analysis for mission-critical autonomous vehicle data platforms.

Own numerical analysis and model parity for compressed and optimized autonomous-driving models. Build tooling for sensitivity, robustness, training-dynamics observability, and numerical diagnostics using Jacobian/Hessian methods. Connect tensor-level differences to vehicle behavior, investigate numerical failures, and make analysis efficient enough for distributed production training and evaluation workflows.

Leads the Planner Evaluation engineering team for autonomous driving systems. Owns the technical vision and multi-quarter roadmap, guides architecture and implementation, establishes simulation and on-road verification strategies, and drives root-cause investigations, metrics, automated testing, and reliable evaluation workflows. Partners cross-functionally with Safety, Systems Engineering, and Product, mentors engineers, and advances software engineering practices supporting GM Super Cruise.

Leads enterprise-scale engineering platforms for embedded software development, including CI/CD, GitOps, Kubernetes, Terraform, progressive delivery, observability, security, and compliance. Defines engineering standards for branching, releases, quality gates, infrastructure, and validation workflows. Provides technical leadership, mentors DevOps and simulation teams, resolves complex cross-team problems, and improves engineering productivity, release confidence, and product quality across software-defined vehicle programs.

Design, build, and operate full‑stack data labeling tools and pipelines for autonomous vehicle ML. Implement scalable UX, automation, ML-driven annotation, and cloud services; own projects end-to-end and collaborate across ML, product, and ops to improve labeling quality and velocity.

Design, implement, benchmark, and optimize CUDA-based GPU kernels and custom operators for on-vehicle ML inference. Build profiling, debugging, and validation tooling, collaborate with compilers, runtime, and architecture teams to deliver high-performance libraries into production, and manage internal customer relationships while maintaining high engineering standards.

Staff software engineer responsible for designing, building, and operating production authorization services. Leads authorization framework development, access-control architecture, security reviews, cloud-native microservices, secure APIs, observability, data modeling, and production support. Establishes engineering standards, reusable security patterns, technical guidance, and deployment practices while mentoring engineers and shaping technology direction. Requires deep expertise in authentication, authorization, RBAC, ABAC, distributed systems, cloud platforms, and secure software development.

Design, develop, deploy, and operate large-scale data platform services supporting autonomous vehicle model training, mining, labeling, and replay. Define and measure SLOs and SLIs, lead incident response, build self-service capabilities, deliver end-to-end technical projects, and contribute to roadmap decisions. Collaborate with ML and data engineers, participate in design reviews, conduct interviews, onboard employees, and mentor junior engineers.

Leads architecture, implementation, and operational excellence for CI/CD, build, test, release, and developer productivity platforms supporting embedded Linux infotainment systems. Designs scalable pipelines, automates releases, builds self-service tooling and dashboards, improves observability, and drives cross-functional initiatives. Mentors engineers, influences technical direction, and advances software delivery reliability across multiple vehicle programs.