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!

Responsibilities

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

Requirements

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
• Experience in autonomous driving, robotics, or other safety-critical real-time ML systems
• Publications or demonstrated technical contributions in efficient ML, autonomous driving, robotics, or related areas
• Prior contributions to large-scale ML systems deployed in production

Compensation

Salary Range: $170,000 - $240,000