Jobs at Gatik

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.

Lead development and execution of test strategies and validation for autonomous vehicle control systems. Manage test engineers, develop automated testing frameworks, ensure traceability, troubleshoot integration issues, and scale HiL/ViL and vehicle-level validation infrastructure to guarantee safety and release quality.

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 and implement real-time perception and sensor-fusion algorithms (LiDAR, camera, radar) for autonomous trucks. Build and optimize deep learning models, manage large-scale datasets, run field tests and simulations, and develop validation frameworks to ensure robust, safe perception in diverse driving conditions.

Provide onsite day-to-day IT support for laptops, desktops, mobile devices, printers, and AV. Configure new-hire equipment and accounts, troubleshoot hardware/software/network issues, maintain inventory and perform routine security/connectivity checks, document processes, and coordinate with vendors for office infrastructure and networking support.

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.

Lead end-to-end manufacturing integration of autonomy hardware into trucks, develop assembly and QA procedures, troubleshoot builds, run inspections and tests, analyze quality data, drive continuous improvement, and coordinate with design teams and contract manufacturers to ensure reliable, traceable production.

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.

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, implement, and optimize real-time motion planning and decision-making systems for autonomous vehicles. Develop lattice-, sampling-, and optimization-based planners, integrate perception/prediction/control constraints, debug in simulation and on-road, and optimize C++ infrastructure for safe, deployable trajectory generation and evaluation.

Lead design and implementation of multi-modal perception: object detection, tracking, and sensor fusion. Build data pipelines, train and optimize perception models, develop scalable training/evaluation tools, and integrate systems for real-world autonomous vehicle deployment.

Design and implement real-time sensor fusion, GPS-IMU fusion, and SLAM-based localization and odometry algorithms for autonomous vehicles. Integrate localization with perception, mapping, and control, validate in simulation and real-world tests, optimize for real-time constraints, and debug using large-scale field data to improve robustness and accuracy.

Design, build, and operate scalable data ingestion, CI/CD, and deployment infrastructure for a growing autonomous vehicle fleet. Implement IaC, containerized deployments, ETL pipelines, simulation integration into CI, and coordinate on-board software upgrades and site rollouts to enable reliable AV software development and production deployments.