ML Engineer, Agentic Labeling

Avala; San Francisco (CA); Hybrid; USD 150,000 - 230,000; Full time

Benefits offered by this job

Equity

Health insurance

Unlimited PTO

FSA

Job summary

Avala is seeking an ML Engineer to build and refine the labeling platform that powers its agentic annotation system. You will work at the intersection of foundation models, active learning, and production annotation workflows.

The role involves developing auto-labeling models (2D, 3D, segmentation), routing tasks between AI and human annotators, and creating feedback loops to steadily improve accuracy and coverage.

Qualifications

• 3+ years training and deploying ML models in production.
• Experience with object detection, segmentation, or 3D perception models.
• Proficiency in Python, PyTorch, and distributed training frameworks.
• Experience with active learning, semi-supervised learning, or human-in-the-loop ML.
• Understanding of ML ops — model versioning, A/B testing, monitoring.

Responsibilities

• Develop and fine-tune foundation models for auto-labeling.
• Build active learning systems routing tasks between AI and humans.
• Design feedback loops using human corrections to improve models.
• Implement model evaluation pipelines for annotation quality and coverage.
• Scale inference infrastructure for real-time auto-labeling across sensors.
• Collaborate with operations to optimize the human-AI workflow.

Skills

Object detection

3D perception

Python

Active learning

MLOps

Distributed training

Tools

PyTorch

Job description

Posted 6 months ago San Francisco, CA full-time

About This Role

Build the ML models that power Avala's agentic labeling system — the pipeline where AI models generate initial annotations, human experts refine them, and the feedback loop continuously improves both. You'll work at the intersection of foundation models, active learning, and production annotation workflows.

What You'll Do:

• Develop and fine-tune foundation models for auto-labeling (2D detection, 3D cuboids, segmentation)
• Build active learning systems that intelligently route tasks between AI and human annotators
• Design feedback loops that use human corrections to improve model accuracy over time
• Implement model evaluation pipelines that measure annotation quality and coverage
• Scale inference infrastructure to handle real-time auto-labeling across sensor modalities
• Collaborate with operations teams to optimize the human-AI annotation workflow

Requirements:

• 3+ years training and deploying ML models in production
• Strong experience with object detection, segmentation, or 3D perception models
• Proficiency in Python, PyTorch, and distributed training frameworks
• Experience with active learning, semi-supervised learning, or human-in-the-loop ML
• Understanding of ML ops — model versioning, A/B testing, monitoring

Nice to Have:

• Experience with LiDAR perception or multi-sensor fusion models
• Familiarity with foundation models (SAM, DINO, or similar) for annotation
• Published research in active learning or interactive annotation

What We Offer

Competitive salary and meaningful equity in the team building Fleet Learning Infrastructure for Physical AI

Full health, dental, and vision coverage, plus FSA and unlimited PTO

Work on the closed-loop data infrastructure that the next generation of autonomous systems learns from

A team that built Tesla Autopilot's data infrastructure, now building something bigger