Top Tech Jobs & Startup Jobs

Own end-to-end validation of autonomous driving behaviors from requirements definition through launch. Evaluate behaviors using simulation, road data, closed-course testing, and expert review across large scenario sets. Define metrics, acceptance criteria, test strategies, and readiness evidence, while partnering cross-functionally on release decisions. The role also involves safety validation, hazard analysis, risk assessment, human benchmarking, counterfactual simulation, and translating regulatory expectations into behavioral requirements and compliance evidence.

Develop and maintain enterprise data-intensive and AI-enabled applications supporting product development and quality teams. Build optimized SQL transformations, Databricks pipelines, Java/Python backend services, APIs, and cloud-native applications using Kubernetes and Docker. Partner with AI teams to productionize ML and LLM solutions, implement testing, CI/CD, observability, security, and reliability practices, troubleshoot production issues, shape architecture, and mentor engineers.

Monitor, triage, and investigate cybersecurity alerts across endpoint, network, cloud, identity, application, and manufacturing environments. Use SIEM, EDR, NDR, SOAR, threat intelligence, and cloud security platforms to identify threats, hunt for malicious activity, and improve detection logic. Create scripts, queries, dashboards, and automation workflows; support tool integrations and incident investigations; document findings; and collaborate with security and engineering teams. Participate in a 24x7 on-call rotation.

Leads a team of autonomy behavior systems engineers and sets technical direction for requirements, simulation, validation, coverage, metrics, acceptance criteria, and release evidence. Develops scalable evaluation systems using synthetic tests, road data, and counterfactual references. Partners with engineering, safety, legal, product, and operations teams on engineering, release, and launch decisions. Builds team capability, provides feedback, and drives alignment across organizational boundaries.

Leads integration and quality assurance testing for ADAS and autonomous vehicle platform software. Responsibilities include integrating hardware, software, and calibration; executing manual and automated milestone and behavior tests; validating control modules in bench and vehicle environments; developing test protocols and scripts; investigating issues; supporting incident resolution; collaborating across agile teams; and mentoring junior engineers. The role includes occasional development trips, plant activities, prototype work, garage testing, and fleet testing.

Build and operate full‑stack labeling tools and pipelines for autonomous vehicle ML. Design scalable UIs and services, integrate ML-driven annotation, create automation and QA tooling, own projects end‑to‑end, and collaborate with ML, Ops, Product, and Data teams to improve labeling quality and velocity.

Leads and develops an engineering team focused on developer productivity and observability for autonomous vehicle software. Defines technical strategy and platform roadmaps, oversees distributed systems architecture, instrumentation, logging, metrics, and tracing, and supports incident response. Partners with AI/ML, firmware, and infrastructure teams to improve reliability. Leads telemetry tools and data pipelines at scale while managing third-party observability vendors, costs, coaching, performance reviews, and career development.

Sets technical direction for counterfactual autonomy behavior validation, including scene reconstruction, human and road-user response modeling, controlled scenario variation, simulation pipelines, outcome estimation, and validation. Converts real incidents into reproducible, auditable scenarios and establishes confidence, traceability, and appropriate use of simulation results. Leads cross-functional work across safety, simulation, human factors, legal, product, and operations to produce engineering actions, safety-case evidence, and release recommendations.

Build and ship driver-facing AI experiences for GM vehicles using Kotlin and Android Automotive. Own features end to end across conversational behavior, on-device fulfillment, vehicle integration, UI, connected services, validation, and iteration. Prototype and productionize intelligent capabilities, diagnose issues across application and cloud boundaries, establish engineering patterns, collaborate cross-functionally, mentor engineers, and influence the roadmap for in-vehicle AI products.

Design, build, and operate scalable hybrid human/machine data-labeling tools and pipelines for autonomous vehicle ML. Implement full‑stack features (frontend, backend, APIs), integrate ML-driven annotation and active learning, build automation and QA tooling, and own projects end-to-end while collaborating with ML, product, and operations teams to improve labeling velocity and quality.