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
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End-to-End Model Development: Own the full ML lifecycle, including data strategy, preprocessing, training, evaluation, optimization, deployment, and monitoring.
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Autonomous Driving Models: Develop and improve models supporting perception, prediction, planning, and scene understanding.
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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.
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Real-Time Deployment: Integrate trained models into C+±based autonomy systems and optimize inference for production vehicle hardware.
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Model Optimization: Profile and optimize neural networks using CUDA, TensorRT, and related technologies.
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Simulation and Evaluation: Analyze model performance using simulation and real-world driving data, identify failure modes, and drive improvements.
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Scalable ML Infrastructure: Build high-throughput pipelines for training, evaluation, data processing, and large-scale offline inference.
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Data Workflows and Tooling: Develop reliable pipelines for dataset curation, annotation, preprocessing, visualization, diagnostics, benchmarking, and continuous feedback from field data.
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Cross-Functional Integration: Partner with autonomy, systems, hardware, and infrastructure teams to ensure ML components integrate reliably into the broader vehicle platform.
What we’re looking for
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Education: MS or PhD in Computer Science, Machine Learning, Robotics, Electrical Engineering, Statistics, Optimization, or a related field.
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Experience: Open to all experience levels. Leveling will be determined based on experience and technical depth.
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Programming & Frameworks:
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Strong Python skills and experience with frameworks such as PyTorch or TensorFlow.
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Strong C++ skills and experience integrating ML models into high-performance production systems.
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Core ML & Systems Expertise:
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Deep understanding of ML workflows, including data curation, training, evaluation, ablation studies, deployment, and inference optimization.
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Experience deploying and optimizing neural networks for real-time, embedded, robotics, autonomous driving, or other performance-constrained systems.
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Experience with model optimization techniques such as quantization, pruning, compression, and efficient architectures.
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Experience with software architecture, profiling, latency optimization, system-level debugging, and data flow analysis.
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Infrastructure & Compute Tools:
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Experience with CUDA and TensorRT is highly desirable.
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Experience with cloud-based ML training and evaluation pipelines, preferably Azure.
Bonus Qualifications:
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Experience with transformers, multimodal models, diffusion models, world models, or end-to-end driving models is a plus.
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Experience in autonomous driving, robotics, or other safety-critical real-time ML systems is strongly preferred.
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Publications or demonstrated technical contributions in efficient ML, autonomous driving, robotics, or related areas are a plus.
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Prior contributions to large-scale ML systems deployed in production.