Top Tech Jobs & Startup Jobs

Leads model distillation, parameter-efficient fine-tuning, reinforcement-learning alignment, dataset development, quantization-aware training, and evaluation for multimodal AI models deployed on automotive edge hardware. Sets architectural direction for optimization pipelines, selects foundation models, manages continuous improvement loops, and ensures compact models retain reasoning, vision, language, accuracy, and safety performance after compression.

Own the functional architecture and solution context for GM’s high-voltage battery controls and software products. Develop requirements architecture, system models, interfaces, APIs, feature decomposition, validation strategies, and roadmap priorities. Lead technical discussions, requirements flowdown, FMEA linkage, and lifecycle engineering while mentoring systems and software requirements engineers. Collaborate across product, systems, controls, hardware, and software teams to resolve complex issues and deliver safe, scalable electrification solutions through agile development.

Leads GM’s autonomous vehicle evaluation organization, defining strategy, roadmaps, methodologies, infrastructure, metrics, risk assessment, and release-gating approaches. Oversees evaluation of perception, prediction, and planning systems using statistical and machine learning methods, scalable data platforms, workflows, and AI techniques. Builds and manages engineering teams, drives architectural decisions, establishes KPIs, aligns cross-functional stakeholders, and translates system-level results into decisions for engineering and executive leadership.

Leads an engineering team developing autonomous vehicle evaluation platforms, metrics, workflows, dashboards, and analysis tools for simulation and on-road testing. Sets technical direction, guides statistical and machine learning evaluation methods, oversees performance analysis across perception, prediction, and planning, and supports release gating and risk assessment. Responsibilities include hiring, coaching, architectural decisions, cross-functional collaboration, code quality, observability, and translating system-level results into actionable engineering and leadership insights.

Lead the safety strategy, validation methodology, and safety case for Level 3–4 autonomous driving systems. Define ODDs, behavior evaluation criteria, safety metrics, sufficiency criteria, and launch targets. Assess AV performance using simulation, closed-course, and public-road data, applying SOTIF, ISO 5083, and AVSC practices. Provide technical leadership across AI/ML, software validation, simulation, metrics, and vehicle program teams while influencing executives and cross-functional stakeholders.

Lead AI, machine learning, and data science workstreams from methodology through production delivery. Develop scalable, maintainable analytical code, explore new methods, influence product decisions, and collaborate with engineering, product, business, and data teams. Translate technical tradeoffs into actionable recommendations, present findings to technical and business audiences, and deliver measurable business impact while managing risks and maintaining methodological quality.

Lead end-to-end machine learning development for autonomous driving, including data curation, foundation-model pre-training, post-training, fine-tuning, alignment, and deployment. Build scalable pipelines using real and synthetic datasets, apply imitation and reinforcement learning, improve model safety and reliability, collaborate across teams, lead technical initiatives, and mentor ML engineers.

Lead development of scalable, reliable continuous integration infrastructure supporting autonomous-vehicle development, machine learning training, simulation, and remote builds. Specialize in Remote Build Execution and a FUSE-based file system, enabling source-code editing and developer workflows. Design and implement productivity improvements, evaluate technologies, influence technical roadmaps, establish engineering best practices, manage technical debt, and mentor engineers while balancing business and customer priorities.

Principal Software Engineer leading architecture and technical strategy for an in-vehicle AI assistant platform. The role spans Android Automotive client applications, cloud services, edge-cloud orchestration, conversational AI, gRPC, streaming interfaces, testing, telemetry, and distributed systems. Responsibilities include cross-functional architectural alignment, roadmap development, resolving complex reliability and privacy trade-offs, integrating AI frameworks, and mentoring senior engineering teams.

Leads core in-vehicle experience platform products and integrates shared system UI capabilities across vehicle programs. Defines product direction, requirements, interaction patterns, and experience architecture while balancing standardization with brand and vehicle variation. Partners with design, engineering, program management, and vehicle teams to deliver cohesive hardware-software experiences. Uses customer research, market analysis, experimentation, and competitive insights to guide strategy, resolve ambiguity, manage dependencies, and drive launches and iteration.