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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Staff Software Engineer, Behavior ML Data - **Company:** Nuro Inc. - **Location:** Mountain View, CA, United States - **Experience:** Expert - **Salary:** $193,930.0 - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Data Analysis, C++ (Programming Language), Data Distribution Service, Information Engineering, Data Mining, Data Systems, Data Visualization, Python (Programming Language), Machine Learning, Tensorflow, DataOps, Data Streaming, Usage Analysis, Data Processing, Data Server Interface, Data Ingestion, Deep Learning, Build Management, Information Technology, Build Tools, Data Management, Machine Learning Operations, Data Pipelines, Data Selection - **Published:** September 29, 2026 - **Apply:** https://www.juju.com/job/16_066c7c975 ## About the Role * 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. ## Description 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. ## Related Videos - [Modern Data Architectures need Software Engineering](https://www.wearedevelopers.com/videos/1030-modern-data-architectures-need-software-engineering) - [Machine learning in the browser with TensorFlowjs](https://www.wearedevelopers.com/videos/155-machine-learning-in-the-browser-with-tensorflowjs) - [Data Science, ML & AI in the Oil and Gas Industry at NDT Global - Dr. Katja Träumner](https://www.wearedevelopers.com/videos/1308-data-science-ml-ai-in-the-oil-and-gas-industry-at-ndt-global-dr-katja-traumner) - [Developing an AI.SDK](https://www.wearedevelopers.com/videos/198-developing-an-ai-sdk) - [Developer Experience, Platform Engineering and AI powered Apps](https://www.wearedevelopers.com/videos/990-developer-experience-platform-engineering-and-ai-powered-apps) - [Building a framework-independent component library](https://www.wearedevelopers.com/videos/1679-building-a-framework-independent-component-library) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production)