Machine Learning Engineer

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.
• Cross-Functional Integration: 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.