Jobs at GenomeKey

Leads the Metrics Frameworks team supporting autonomous vehicle evaluation. Responsibilities include hiring and developing software and data engineers, setting technical direction, owning scalable metrics platforms and data pipelines, establishing reliability and observability practices, managing roadmaps and technical debt, and partnering with stakeholders to deliver trustworthy evaluation capabilities.

Lead architecture and hands-on delivery of cloud-native mapping data platforms and Agentic AI solutions. Design scalable ingestion, storage, APIs, governance, quality, lineage, observability, and infrastructure-as-code practices. Build reusable services, AI workflows, and knowledge-grounded agents while ensuring security, reliability, cost efficiency, and responsible deployment. Partner across mapping, software, data science, and platform teams, lead technical reviews, establish standards, and mentor engineers.

Leads organizational and employee research using behavioral science, advanced analytics, surveys, causal methods, longitudinal and multilevel analysis. Studies engagement, sentiment, workforce dynamics, manager effectiveness, and people interventions. Integrates HR and employee data, evaluates programs, develops reusable research methods and data products, and translates findings into recommendations for business and HR leaders. Maintains reproducible, well-documented analyses while handling sensitive employee information.

Lead architecture and development of GM’s vehicle Core OS platform, including hypervisor integration, virtual machines, guest operating systems, secure boot, resource governance, container runtime capabilities, health monitoring, and fault recovery. Optimize and debug embedded systems across hardware, firmware, hypervisors, kernels, and user space. Drive CI/CD, automated testing, HIL validation, technical reviews, cross-team execution, and engineer mentorship.

Develop software for vehicle-level functions and perform integration and verification testing with minimal direction from lead engineers. The role supports General Motors’ autonomous vehicle launch efforts and requires contributing to software development and system validation in an automotive environment.

Principal Software Engineer serving as technical authority and architect for GM’s vehicle Core OS platform. Defines architecture and roadmaps for hypervisors, virtualization, multi-OS support, safety partitioning, secure boot, containers, workload management, and platform performance. Leads technology selection, supplier relationships, cross-team investigations, architecture reviews, and production optimization across software and silicon. Mentors senior engineers and establishes engineering standards for a platform supporting Linux, Android, and QNX in safety-critical vehicle systems.

Leads and builds GM’s Core OS engineering organization for vehicle compute platforms. Owns the roadmap and delivery of hypervisors, virtualization, secure boot, workload management, safety monitoring, and support for Linux, Android, and QNX. Establishes architecture, hires engineers, manages vendors, sets platform KPIs, and coordinates with silicon, cybersecurity, safety, application platform, and vehicle program teams to deliver production milestones.

Designs, develops, and operates Java and Spring Boot microservices, APIs, and event-driven integrations supporting GM mobile experiences. Deploys cloud-native services on Azure, strengthens CI/CD automation, improves observability and reliability, applies AI-enabled engineering practices, and delivers thoroughly tested software. Partners across teams on architecture, security, and product delivery while mentoring engineers and contributing to documentation, operational standards, and production support.

Lead the architecture and development of high-performance backend services for GM Commercial Services software. Design scalable, reliable systems, APIs, and products; solve complex organizational problems; improve engineering practices, testing, documentation, and monitoring; launch customer-focused products; collaborate across product, design, infrastructure, and operations; and mentor engineers.

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.

Leads a team of autonomy behavior systems engineers responsible for requirements, simulation, validation, metrics, coverage, and release evidence for autonomous vehicles. Sets technical direction, develops scalable evaluation systems, partners with engineering, safety, legal, product, and operations teams, and supports release and launch decisions. The manager also develops engineers, builds alignment across organizations, and guides safety-case, risk, compliance, and validation lifecycle activities.

Build and operate full-stack vehicle software integrity systems using NextJS and Go. Develop data visualizations and dashboards, extend shared component libraries, collaborate with product and design teams, write tested code, manage deliverables, and mentor engineers. The role requires architectural ownership, strong software engineering practices, responsible use of AI tools, and effective decision-making amid evolving priorities.

Recruits interns, co-ops, new graduates, and other early-career talent across technical and business roles. Manages requisitions through sourcing, screening, interviews, offers, and pre-employment activities. Builds university and student organization relationships, supports campus recruiting, coordinates stakeholders, maintains recruiting data in Workday, and uses recruiting metrics to improve hiring processes and candidate experience.

Lead development of behavior validation products and dashboards for autonomous vehicle launch decisions. Partner with engineering, safety, product, and Embodied AI teams to define roadmaps, prioritize improvements using Pareto analysis, establish success metrics, and implement scalable technical solutions. Use Python for prototyping and data analysis while connecting data pipelines to user-facing interfaces and ensuring validation tools are reliable and effective.

Design and build scalable, reliable ML training infrastructure for advanced AI research and large-model development. Optimize distributed training performance, resource utilization, data loading, observability, debugging, and operational excellence across heterogeneous GPU and cloud environments. Collaborate with machine learning engineers, research scientists, and cross-functional teams to integrate new platform capabilities and enable intelligent driving technologies.

Design and build scalable, high-performance Golang software supporting enterprise data architecture. Lead technical architecture, collaborate across teams, mentor engineers, resolve complex technical problems, and support Agile delivery. Ensure code quality through reviews, automated testing, CI/CD, and secure software development practices. Contribute to technical roadmaps, enterprise architecture, operational support, and continuous improvement across the full software development lifecycle.

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.

Conduct advanced AI and machine learning research for autonomous driving. Design and prototype state-of-the-art algorithms, influence technical roadmaps, mentor researchers and engineers, collaborate across multidisciplinary teams, and represent GM through publications, patents, and conference participation.

Leads a cross-functional team developing behavior critics and human benchmarking systems for autonomous vehicles. Designs, deploys, and operates ML models for behavioral inference; establishes technical roadmaps; and integrates evaluation models with autonomy, simulation, safety, product, validation, release, and monitoring workflows. The role requires production ML pipeline expertise, people management, and collaboration across safety-critical autonomous driving systems.

Develop 2D and 3D simulation platforms for autonomous mobile robots, create realistic scenarios and edge cases, integrate navigation software, debug simulated robot behavior, analyze results, and build regression testing workflows. Collaborate with robotics and AI teams to improve system validation and robustness. 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.