Senior Machine Learning Engineer, Auto Labeling
Serve Robotics
full-time
Posted on:
Location Type: Remote
Location: Remote • California • 🇺🇸 United States
Visit company websiteSalary
💰 $155,000 - $200,000 per year
Job Level
Senior
Tech Stack
PythonPyTorchTensorflow
About the role
- 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.
Requirements
- 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.
Benefits
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Applicant Tracking System Keywords
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Hard skills
auto-labeling pipelines3D annotations4D annotationsdata-centric learning workflowsself-trainingweak supervisionsimulation-to-real adaptationdeep learningPyTorchTensorFlow
Soft skills
collaborationcommunicationmentoringsupporting ML engineerscross-functional teamwork
Certifications
M.S. in Computer SciencePh.D. in Computer ScienceM.S. in Machine LearningPh.D. in Machine LearningM.S. in RoboticsPh.D. in Robotics