At Serve Robotics, we’re reimagining how things move in cities. Our personable sidewalk robot is our vision for the future. It’s designed to take deliveries away from congested streets, make deliveries available to more people, and benefit local businesses.
The Serve fleet has been delighting merchants, customers, and pedestrians along the way in Los Angeles, Miami, Dallas, Atlanta and Chicago while doing commercial deliveries. We’re looking for talented individuals who will grow robotic deliveries from surprising novelty to efficient ubiquity.
We are tech industry veterans in software, hardware, and design who are pooling our skills to build the future we want to live in. We are solving real-world problems leveraging robotics, machine learning and computer vision, among other disciplines, with a mindful eye towards the end-to-end user experience. Our team is agile, diverse, and driven. We believe that the best way to solve complicated dynamic problems is collaboratively and respectfully.
Serve Robotics is seeking a Senior Machine Learning Engineer, Auto-Labeling to lead the design and implementation of cutting-edge ML models and scalable systems that enable 4D auto-labeling for autonomous foundational models. This role focuses on developing robust and efficient algorithms for automatic generation of supervision information to power the data pipelines for next-generation end-to-end autonomy models. By bridging data engineering, ML systems, and model deployment, this position enables Serve to leverage every robot mile for faster, smarter, and more efficient autonomy development.
Responsibilities
Design and implement production-grade auto-labeling pipelines that generate 3D and 4D annotations (objects, trajectories, maps) from multi-modal robot data at scale.
Develop data-centric learning workflows that connect auto-label outputs, Serve’s dataset infrastructure, and continuous E2E model training and evaluation pipelines.
Lead initiatives in self-training, weak supervision, and simulation-to-real adaptation to reduce manual labeling dependency and accelerate model iteration cycles.
Collaborate cross-functionally with multiple autonomy teams(e.g ML-infra, mapping, simulation) to align labeling infrastructure with model training and evaluation workflows.
Stay ahead of emerging trends in E2E autonomy and data-centric ML, identifying opportunities to productionize state-of-the-art techniques.
Mentor and support ML engineers and interns in developing robust data-centric practices, from dataset curation and labeling feedback loops to model monitoring and continuous improvement.
Qualifications
M.S. or Ph.D. in Computer Science, Machine Learning, Robotics, or related field, or equivalent industry experience.
5+ years of experience developing production ML systems, preferably in autonomous driving, robotics, or large-scale data platforms.
Strong background in deep learning (PyTorch/TensorFlow) and scalable ML system design (distributed training, dataflow orchestration, and CI/CD for ML).
Hands-on experience with multi-modal sensor data (LiDAR, camera, IMU, odometry) and end-to-end model architectures.
Strong programming skills in Python and solid software engineering fundamentals (testing, versioning, modularity).
Excellent collaboration and communication skills across autonomy, data, and infrastructure teams.
What Makes You Stand Out
Experience with transformer-based models and E2E self-driving architectures.
Contributions to large-scale robotics or autonomous driving ML stacks.
Background in self-supervised learning, active learning, or semi-automated labeling systems.
Expertise in cloud-native ML pipelines (GCP, AWS, or Azure) and containerization/orchestration frameworks (Docker, Kubernetes, Airflow, Ray).
Familiarity with simulation data integration (CARLA, UE5, or internal resim environments).
* Please note: The base salary range listed in this job description reflects compensation for candidates based in the San Francisco Bay Area. While we prefer candidates located in the Bay Area, we are also open to qualified talent working remotely across the:
United States - Base salary range (U.S. – all locations): $155k - $200k USD
Canada - Base salary range (Canada - all locations): $130k - $165k CAD
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