Staff Software Engineer, Behavior ML Data
$193.93k - $352.29kFull-time
Nuro
Who We Are Nuro believes self-driving vehicles are the most immediate and profound opportunity for AI to drive positive change in the physical world. Safer streets, more time for what matters, and easier access to the world around us, that’s why we’re building a universal autonomy platform: self-driving for all roads and all rides. Founded in 2016, Nuro is a physical AI company developing Level 4 autonomous driving technology for a wide range of vehicles, use cases, and markets. Powered by the Nuro Driver™, our universal autonomy platform enables the global mobility ecosystem to deploy autonomy at scale, from robotaxis and logistics fleets to personal vehicles. With years of real-world deployment experience and a flexible, partner-led business model, Nuro is working toward a future where millions of autonomous vehicles powered by our technology help make everyday life safer, easier, and more connected. Nuro has raised over $2B in capital from Uber, NVIDIA, Google, Softbank, Fidelity, T. Rowe Price, and other leading investors. About The Role We are looking for a Senior/Staff Software Engineer to serve as a technical leader for Nuro’s ML Data engine. You will sit at the critical intersection of Autonomy, Machine Learning, and Infrastructure, acting as an architect for the systems that feed our autonomy AI models. In this role you will be a member of the Autonomy team responsible for executing the technical strategy for transforming massive amounts of autonomy data into high-value training signals for autonomy decision making. You will design and build data products for autonomy researchers, develop queries for rare "needle-in-a-haystack" scenarios, and trigger labeling and data ingestion workflows without human intervention. You will partner directly with Autonomy ML researchers to understand their data needs, collaborate with infrastructure teams to define the right data interfaces and APIs, and build robust data selection, simulation, and introspection tools that can process data at scale. If you love solving challenging new problems with a mindset of deriving practical solutions to be used in the physical world, come join us About The Work
- Data Pipeline Architecture: Design and build scalable data ingestion and processing pipelines that turn data streams into targeted training datasets. Lead initiatives to improve data quality, detect anomalies, and manage out-of-distribution examples to ensure robust model training and deployment.
- Cross Functional Leadership: Work across autonomy teams and data infra teams to build effective ML data pipelines and products for ML engineers.
- ML Tooling & Introspection: Develop infrastructure and visualization tools that allow ML researchers to easily introspect data, identify model failure modes, query for new data samples, and understand data distribution shifts.
- Labeling Operations Integration: Collaborate closely with the data operations team to define quality standards, automate quality control (QC), and streamline the feedback loop between model performance and annotation guidelines.
- Active Learning & Data Mining Engines: Lead the engineering effort to operationalize research-grade active learning methods. E.g. build systems that compute embeddings or run inference at scale, manage vector databases, and automatically sample the most informative data points for labeling.
- 7+ years of experience with a proven track record of technical leadership architecting and delivering complex, multi-system ML data engineering data systems.
- Education: B.S./M.S. in Computer Science, Artificial Intelligence, Electrical Engineering, Robotics, or equivalent practical experience.
- Understanding of end-to-end ML data pipelines and their interaction with model training and evaluation.
- Strong proficiency in C++ and Python, with petabyte-level data management experience.
- Experience taking data concepts (e.g., "uncertainty sampling") and turning them into stable, 24/7 production services.
- Prior experience working in large companies with productionized AI systems working on data engines for large scale machine learning.
- Experience in workflow orchestration, introspection UI/UX for data understanding, and ML frameworks for foundation model training.
- Expertise in data-centric AI topics (active learning, pre-training) and their application in autonomous systems.
- You have subject matter expertise and research in one or more of the following areas: Machine Learning, Deep Learning, Robotics , and have some familiarity with the state of the art in ML for autonomous driving and data utilization.
Vacancy posted 5 days ago
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