Senior Simulation Infrastructure Engineer

Gatik is in the business of autonomous freight, and that business is already on the road.

Every day, Gatik’s autonomous trucks move freight for Fortune 50 retailers, grocers and consumer brands across Texas, Arizona, Arkansas and Canada, connecting distribution centers and stores and keeping goods moving. At the heart of it all is the Gatik Driver™, our proprietary Level 4 autonomous driving system, powered by scalable, interpretable AI and engineered to deliver safe, reliable, high-frequency freight operations.

Gatik’s momentum is real. With more than $600 million in contracted revenue and $200 million raised in our August 2026 Series D round, Gatik has taken one of the toughest challenges in autonomous technology and turned it into a proven commercial business.

And the market is paying attention. In 2026, Gatik earned a place on Forbes' America's Best Startup Employers list, and the Inc. 5000 list of America's fastest-growing private companies. The Gatik Driver™ won an Edison Award for Advancements in Autonomous Driving, and Inc. named our Co-founder and Chief Engineer, Apeksha Kumavat, to its Female Founders 500 list.

There's a lot more road ahead, and some of the brightest, most driven minds in autonomous technology are building the next chapter at Gatik. We're taking on hard engineering problems, pushing the limits of what's possible and building on a commercial foundation that's already proven itself in the real world with some of the biggest companies in the world, like PepsiCo and Loblaw. If you're ambitious, love solving problems that matter and want to see your work make a real difference, you'll find plenty to get excited about here.

We are seeking a high impact, technically deep Machine Learning Engineer to develop, optimize, and deploy production ML models across our autonomous vehicle (AV) stack. This role is ideal for engineers who enjoy building models end-to-end - from data and training through optimization and real-time deployment on autonomous vehicles.

You will work closely with perception, prediction, planning, infrastructure, systems, and hardware teams to ensure models are efficient, scalable, reliable, and production-ready for both on-vehicle and cloud workflows.

This role is onsite 5 days a week at our Santa Clara, CA office!

• End-to-End Model Development: Own the full ML lifecycle, including data strategy, preprocessing, training, evaluation, optimization, deployment, and monitoring.
• Autonomous Driving Models: Develop and improve models supporting perception, prediction, planning, and scene understanding.
• Efficient Neural Network Design: Optimize models using techniques such as quantization, pruning, sparsification, compression, and efficient architecture design to meet strict latency, compute, memory, and power constraints.
• Real-Time Deployment: Integrate trained models into C++-based autonomy systems and optimize inference for production vehicle hardware.
• Model Optimization: Profile and optimize neural networks using CUDA, TensorRT, and related technologies.
• Simulation and Evaluation: Analyze model performance using simulation and real-world driving data, identify failure modes, and drive improvements.
• Scalable ML Infrastructure: Build high-throughput pipelines for training, evaluation, data processing, and large-scale offline inference.
• Data Workflows and Tooling: Develop reliable pipelines for dataset curation, annotation, preprocessing, visualization, diagnostics, benchmarking, and continuous feedback from field data.
• CrossFunctionalIntegration: Partner with autonomy, systems, hardware, and infrastructure teams to ensure ML components integrate reliably into the broader vehicle platform.

• Education: MS or PhD in Computer Science, Machine Learning, Robotics, Electrical Engineering, Statistics, Optimization, or a related field.
• Experience: Open to all experience levels. Leveling will be determined based on experience and technical depth.
• Programming & Frameworks:
• Strong Python skills and experience with frameworks such as PyTorch or TensorFlow.
• Strong C++ skills and experience integrating ML models into high-performance production systems.
• Core ML & Systems Expertise:
• Deep understanding of ML workflows, including data curation, training, evaluation, ablation studies, deployment, and inference optimization.
• Experience deploying and optimizing neural networks for real-time, embedded, robotics, autonomous driving, or other performance-constrained systems.
• Experience with model optimization techniques such as quantization, pruning, compression, and efficient architectures.
• Experience with software architecture, profiling, latency optimization, system-level debugging, and data flow analysis.
• Infrastructure & Compute Tools:
• Experience with CUDA and TensorRT is highly desirable.
• Experience with cloud-based ML training and evaluation pipelines, preferably Azure.

• Experience with transformers, multimodal models, diffusion models, world models, or end-to-end driving models is a plus.
• Experience in autonomous driving, robotics, or other safety-critical real-time ML systems is strongly preferred.
• Publications or demonstrated technical contributions in efficient ML, autonomous driving, robotics, or related areas are a plus.
• Prior contributions to large-scale ML systems deployed in production.

Salary Range $170,000 - $240,000