Jobs at Gatik

Lead hardware safety architecture, reliability engineering, and functional safety compliance for autonomous vehicle compute, sensors, power distribution, and networking systems. Conduct FMEDA, PMHF, SPFM, FTA, fault injection, and worst-case circuit analyses; define safety requirements; evaluate automotive-grade components and vendors; and produce safety manuals, verification documentation, and safety-case evidence. The role is onsite in Santa Clara five days per week.

Build and maintain hardware-in-the-loop test benches for autonomous vehicle electronics, including ECUs, sensors, production compute units, and drive-by-wire systems. Develop sensor simulation and hardware fault-injection suites, automate Python and C++ regression testing in CI/CD pipelines, and perform ISO 26262 requirements-based verification. Investigate hardware-software interface defects, communication anomalies, and bus timing issues with engineering teams.

Own the safety architecture and integration of drive-by-wire systems for autonomous trucks, including redundant steering, braking, propulsion, and chassis actuation. Define fault reactions, degraded modes, minimum risk maneuvers, and safety requirements. Conduct HARA, FMEA, and FTA analyses; integrate OEM and supplier systems; ensure ISO 26262 ASIL D compliance; and support on-vehicle fault-injection validation under demanding operating conditions.

Lead onsite closed-course verification and validation testing for autonomous commercial trucks. Plan and execute proving-ground programs, operate robotic soft targets, validate safety-critical maneuvers, analyze vehicle dynamics and autonomy telemetry using Python and MATLAB, track issues, and enforce site safety protocols. The role requires extensive vehicle-level testing experience, automotive data acquisition expertise, and strong safety and risk-mitigation practices.

Leads technical training infrastructure across autonomous vehicle operational sites. Develops standardized curricula, translates engineering materials and SOPs into training, ensures safety and procedural compliance, audits trainers, tracks competencies, maintains training records, and manages retraining and recertification. The role supports testing, deployment, and engineering teams while standardizing training across multiple locations and requires frequent travel.

Lead systems and safety engineering for a Level 4 autonomous vehicle platform. Own safety architecture, safety cases, risk management, AI/ML safety, requirements, verification, regulatory engagement, supplier interfaces, customer safety programs, and operational governance. Build and manage a multidisciplinary engineering team while coordinating with internal teams, OEMs, Tier 1 suppliers, regulators, auditors, and commercial customers.

Lead enterprise-wide product lifecycle and configuration management across hardware, software, and field operations. Build scalable change-control processes, oversee traceability and release management, integrate PLM, ERP, and QMS systems, and coordinate Change Control Board activities. Ensure product baselines, software and hardware dependencies, safety validations, compliance records, and operational procedures remain accurate and audit-ready across the organization.

Leads network design and optimization for autonomous freight operations. Analyzes customer and network data, develops routing and scheduling solutions, supports B2B sales proposals, and delivers strategic recommendations to leadership. Drives scalable tools and processes, manages cross-functional projects, translates business requirements for technical teams, and researches market and industry trends. The role requires strong quantitative problem-solving, communication, and stakeholder management skills in a fast-paced autonomous logistics environment.

Develop, optimize, and deploy production machine learning models for autonomous vehicle perception, prediction, planning, and scene understanding. Own the full ML lifecycle, build scalable training and data pipelines, optimize neural networks with quantization and TensorRT, and integrate models into C++ vehicle systems. Analyze simulation and real-world driving data, improve reliability and latency, and collaborate with autonomy, hardware, systems, and infrastructure teams.

Own the functional safety lifecycle and system safety concepts for a Level 4 autonomous driving platform. Lead hazard analysis, STPA, FMEA, fault tree analysis, requirements traceability, safety metrics, and minimum-risk maneuver architecture. Define fail-operational and fail-safe strategies across perception, localization, mapping, planning, controls, and fault management. Collaborate with software, middleware, infrastructure, and systems teams to support safe autonomous releases.

Lead Gatik’s end-to-end verification and validation architecture for autonomous vehicle systems. Own the V&V framework, Verification Traceability Matrix, safety-case evidence, and driverless release sign-offs across SIL, HIL, track, and public-road testing. Govern testing strategies, ensure requirements traceability, coordinate cross-functional teams, and prepare audit-ready validation packages aligned with UL 4600, ISO 26262, and ISO 21448.

Design and scale software-in-the-loop testing and simulation pipelines for autonomous vehicle software. Create synthetic scenarios, replay fleet logs, perform permutation testing, verify perception, planning, controls, and mapping, and assess simulator fidelity. Develop automated dashboards tracking safety metrics, coverage, and release readiness. The role requires onsite work five days per week in Santa Clara and collaboration across simulation and autonomy platform teams.

Lead AI/ML safety engineering for Level 4 autonomous vehicles. Define SOTIF and functional safety strategies, derive requirements for learned systems, design runtime safety monitors and OOD detectors, assess dataset coverage and bias, analyze ML failure modes, and build evidence-backed safety cases. The role requires collaboration across perception, prediction, planning, systems, and safety engineering teams, with onsite work five days per week in Santa Clara, California.

Owns financial reporting, dashboards, KPIs, forecasts, budgeting, scenario analysis, and financial modeling. Partners with cross-functional teams to improve projections, supports consolidated financial statements, monthly and quarterly close, audits, compensation analysis, and Board and investor materials. The role reports to the CFO and is onsite five days per week in Santa Clara, California.

Develop and integrate embedded chassis-control software for autonomous vehicles and ECU hardware. Responsibilities include functional-safety development, model-based toolchain design, release planning, testing, vehicle bring-up, calibration, data analysis, debugging, and hardware/software integration. The engineer will validate AV components through on-vehicle, simulation, SiL, and HiL testing while collaborating with requirements, software, hardware, and systems engineers to support safe, reliable Level 4 autonomous vehicle operation.

Lead design and implementation of a multi-modal behavior prediction system using image and LiDAR data. Build optimized data pipelines, train and evaluate models (LSTM, Transformer), scale training/inference infrastructure, and integrate prediction into the autonomous driving stack. Drive R&D to production and ensure system reliability and safety through testing.

Build and improve HD mapping production pipelines: process and validate large LiDAR/camera datasets, extract road features, validate topology, develop Python geospatial tools, use PostGIS and cloud infrastructure, and collaborate with autonomy, perception, and planning teams to deliver high-quality maps on tight timelines.

Design, build, and scale the AI platform for distributed multi-GPU training, experiment tracking, and model deployment. Optimize GPU clusters, networking (NCCL/InfiniBand), Kubernetes-native scheduling, and inference serving (TensorRT, Triton). Develop agentic automation for cluster health, CI/CD, and data curation while integrating ML lifecycle tooling and observability.

Integrate and maintain a ROS2-based autonomy stack inside high-fidelity simulators, author dynamic adversarial scenarios, configure simulated sensors, enable headless Docker orchestration for large-scale cloud testing, stream simulation states to a web dashboard, and manage serialized ROS2 message logging for analysis and playback.

Design, build, and operate large-scale GPU/TPU cloud infrastructure and data pipelines to support distributed training, MLOps, model serving, and observability. Manage Kubernetes clusters, GitOps CI/CD, experiment tracking, and optimize networking and multi-node training performance for research and production workloads.