Senior Machine Learning Engineer, End‑to‑End Autonomous Driving

About the Role

We are seeking a Senior Machine Learning Engineer to join our end‑to‑end autonomous driving team. You will help build, train, and deploy large‑scale E2E driving models that leverage VLM/VLA architectures, and build a data flywheel that continuously improves our systems in the real world!

What you’ll be doing:

• Designing, implementing, and training large‑scale end‑to‑end driving models.
• Driving the data flywheel: identifying failure cases, specifying data collection and labeling needs, and iterating models to close real‑world performance gaps.
• Building, curating, and maintaining high‑quality multimodal datasets (e.g., video, sensor, language/action traces) tailored for end‑to‑end autonomous driving.
• Developing and applying data‑centric learning algorithms such as active learning, curriculum learning, automated hard‑example mining, outlier and novelty detection, and semi/self‑supervised methods.
• Exploring and productizing new data sources including simulation, synthetic data, and world‑model‑based generation/augmentation to improve coverage and robustness.
• Designing and implementing agentic data workflows that automate data discovery, labeling, evaluation, and retraining to maximize development velocity.
• Foster collaborative partnerships with our researchers and engineers, transforming innovative research into robust, industrial-strength machine learning models.

What we need to see:

• PhD with 4+ years, MS with 6 years, or BS (or equivalent experience) with 8+ years of relevant experience in Computer Science, Computer Engineering, or a related technical field.
• Strong background in modern deep learning, including transformer‑based architectures, video modeling, and multimodal VLM/VLA or foundation models.
• Hands‑on experience training and deploying deep learning models on real‑world datasets: data preprocessing, distributed training, evaluation, debugging, and iterative improvement.
• Practical experience with at least some data‑centric methods such as active learning, curriculum learning, outlier/novelty detection, or large‑scale sample mining.
• Proficiency in Python and at least one major deep learning framework (PyTorch, TensorFlow, or JAX), plus solid software engineering practices (testing, code review, CI/CD).
• Demonstrated ability to collaborate effectively across teams, drive designs from prototype to production, and communicate clearly with technical and non‑technical partners.
• Track record of leading complex cross‑team projects, setting technical direction, and making critical technical decisions that impact multiple teams or products.

Ways to stand out from the crowd:

• Experience building and operating data flywheels or large‑scale data pipelines for ML, including data quality monitoring and continuous retraining loops.
• Direct experience with end‑to‑end driving models, large‑scale behavior cloning, or reinforcement/imitation learning for driving or robotics.
• Experience leveraging simulation, synthetic data, or world models to generate training and evaluation data for autonomous systems.
• Contributions to sophisticated methods in data‑centric ML, VLM/VLA, or autonomous driving, such as impactful publications, open‑source projects, or widely used internal tools.
• Background with safety, reliability, and validation requirements for autonomous driving or other safety‑critical applications.