ML Engineer, Agentic Labeling

MaC VC; San Francisco (CA); On-site; USD 140,000 - 230,000; Full time

Job summary

Avala is seeking a talented ML engineer to build the ML models powering its agentic labeling system in SF. You will work at the intersection of foundation models, active learning, and production annotation workflows.

Join a cross-disciplinary team to design, implement, and optimize end-to-end ML pipelines, with a focus on scalable inference and robust evaluation. You will collaborate with operations to streamline human-AI annotations.

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

Skills

Python

PyTorch

Active learning

Distributed training

ML in production

Job description

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