Machine Learning Engineer

About the role

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!

What you'll do

• 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.

What we're looking for

• 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.

Bonus Qualifications

• 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

More about Gatik

We have delivered complete, proprietary AV technology - an integration of software and hardware - to enable earlier successes for our clients in constrained Level 4 autonomy. By choosing the middle mile – with defined point-to-point delivery, we have simplified some of the more complex AV challenges, enabling us to achieve full autonomy ahead of competitors. Given extensive knowledge of Gatik’s well-defined, fixed route ODDs and hybrid architecture, we are able to hyper-optimize our models with exponentially less data, establish gate-keeping mechanisms to maintain explainability, and ensure continued safety of the system for unmanned operations.