Senior Machine Learning Engineer

About the role

You will be a part of a small, production-minded ML team based in Orange County/Oakland. You’ll collaborate with other engineers and researchers to develop, evaluate, and help deploy vision models for tasks like semantic/instance segmentation and object/damage detection across 2D and 3D data.

Experience & Skills Required

Deep ML / CV Fundamentals: You need hands-on experience training and evaluating deep models for segmentation and detection (PyTorch). You must understand how Transformer/LLM building blocks map to vision (ViT/DETR/Mask2Former) and have practical exposure to 2D/3D data, point clouds, and camera geometry

Curiosity & Strict Attention to Detail: You are obsessed with corner cases. You have a sharp eye for data anomalies, run rigorous ablations, keep meticulous experiment logs, and can clearly communicate trade-offs

AI-Empowered, Not AI-Dependent: We strongly encourage leveraging AI tools (Copilot, ChatGPT, Claude) to maximize your efficiency. However, you must 100% understand the underlying details of the code you ship. We are looking for strong independent thinkers and debuggers, not someone who simply passes along AI outputs without deep comprehension

• Working knowledge of transformer and LLM building blocks applied to vision, including self-attention, positional encodings, tokenization, and mapping these ideas to vision models (e.g., ViT, DETR, Mask2Former)
• Practical exposure to 3D/depth data, including familiarity with point clouds, camera geometry (intrinsics/extrinsics), basic calibration, and multi-view geometry
• Proficiency in Python and the relevant tech stack: PyTorch, torchvision, Detectron2 or MMDetection/Segmentation, and Hugging Face Transformers
• Experience with Python services (FastAPI/Flask), Docker, and AWS services (S3, Batch/EC2, ECR) is preferred.
• Strong communication skills with the ability to write tidy PRs, experiment logs, and short design notes to ensure reproducibility

Responsibilities

• The Work: Implement training loops, curate datasets, drive high-priority experiments, and partner with cross-functional teams to close feedback loops from edge cases
• The Stack: PyTorch, Detectron2 / MMDetection / Segmentation, Hugging Face Transformers, Python (FastAPI), Docker, AWS
• Collaborate on model development by implementing training loops, losses, augmentations, and evaluations using PyTorch
• Keep current with the industry by summarizing relevant papers and PRs, and proposing small, testable improvements
• Contribute to datasets by helping define labeling guidelines, curating splits, running quality checks, and maintaining data versioning
• Run experiments to track metrics, perform ablations, write clear experiment notes, and present findings.
• Provide production support by exporting models, writing basic inference code, adding tests, and assisting with performance profiling
• Work cross-functionally, partnering with backend engineers on APIs, containers, and CI, and with ops/labeling teams on edge cases and feedback loops

Benefits

• Competitive salary and equity package
• Comprehensive health and dental insurance
• Retirement savings plan.
• Paid time off and holidays

• We celebrate diversity and are committed to creating an inclusive environment for all employees.
• We encourage qualified candidates from all backgrounds to apply and join us in our mission.
• If you require accommodation at any stage of the application process due to a disability, please let us know.

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