Summary
What you’ll impact
The role is a Machine Learning Engineer focused on developing, optimizing, and deploying production ML models for autonomous vehicle systems. The engineer will own the end-to-end ML lifecycle, work across perception, prediction, planning, and integrate models into real-time C++ vehicle platforms. The position is onsite in Santa Clara, CA.
Responsibilities
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.
Requirements
What you’ll bring
- 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.
- 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.
- 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.
- 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.