Bot Auto
Software Engineer, Machine Learning Infrastructure
Houston, TX or San Francisco Bay Area Based
Sponsorship not specifiedDetected 19 days ago
PythonC++Distributed SystemsFull-Stack DevelopmentKubernetesMachine LearningDeep LearningSparkMLOpsA/B TestingSystems EngineeringResearch
About the role
- With the agility of a start-up and the wisdom of seasoned experts, Bot Auto boasts a team that has achieved numerous world-firsts and unparalleled innovations.
- Join us and transform your dreams into reality.
- The ideal candidate combines strong systems engineering skills with a deep understanding of ML Workflows/Ops and large-scale data infrastructure.
Responsibilities
- Evaluation Platform - Architect and own a scalable, end-to-end model evaluation platform for perception and prediction models central to autonomous driving. Define metrics, design for scale, and make results actionable for researchers.
- Training Infrastructure - Partner with research scientists to optimize and scale distributed training workflows. Integrate experiment tracking and reproducibility into the model lifecycle from day one.
- Dataset & Feature Store - Design and maintain a versioned, high-quality training data store that accelerates model development and supports rapid iteration.
- ML Pipelines - Build automated pipelines spanning data preparation, model training, validation, and deployment - enabling fast experimentation and reproducible outcomes.
- MLOps - Develop production ML services that treat models as products - with reliability, observability, and continuous improvement built in.
- Maintain and evolve a robust data storage and access layer (S3 data lake, Delta Lake) underpinning annotation, evaluation, and training workflows.
- Build scalable, reliable data collection pipelines supporting diverse vehicle dispatch missions.
- Develop foundational services and packages that provide clean, performant access to autonomous driving data across the stack.
- Evaluation Platform - Architect and own a scalable, end-to-end model evaluation platform for perception and prediction models central to autonomous driving.
- Define metrics, design for scale, and make results actionable for researchers.
Requirements
- Strong Programming Skills: Strong proficiency in Python
- working knowledge of C++
- Strong experience with distributed computing and container orchestration - Kubernetes, Spark, or comparable frameworks.
- Bachelor's or Master's in Computer Science, or equivalent practical experience.
- Required:
- Educational Background: Bachelor's or Master's in Computer Science, or equivalent practical experience.
- ML/DL Infrastructure Experience - Demonstrated hands-on experience building or scaling at least one of the following in a production environment:
- Annotation platforms - tooling or pipelines that support high-throughput, high-accuracy labeling workflows.
- Distributed Systems - Strong experience with distributed computing and container orchestration - Kubernetes, Spark, or comparable frameworks.
- Ability to operate independently: scope ambiguous problems, make sound architecture decisions, and drive them to completion.
- Preferred:
Skills
- Evaluation platforms - automated model benchmarking, metric computation, and regression tracking across model versions.
- Training infrastructure - distributed training pipelines, experiment tracking, and model lifecycle management (e.g. W&B, MLflow, ClearML).
- Dataset curation & feature stores - versioned dataset management, data lineage, and tooling for high-quality training data at scale.
- Strong proficiency in Python; working knowledge of C++
- C++ experience in performance-sensitive or safety-critical applications
- Full-stack service development experience.
- Prior work in autonomous driving or robotics.
Benefits
- Machine Learning & Deep Learning Infrastructure
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