Senior Machine Learning Engineer

Kinetic•Costa Mesa, CA

10d•Hybrid

About The Position

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.

Requirements

Hands-on experience training and evaluating deep models for segmentation and detection (PyTorch)
Understanding of how Transformer/LLM building blocks map to vision (ViT/DETR/Mask2Former)
Practical exposure to 2D/3D data, point clouds, and camera geometry
Obsession with corner cases, sharp eye for data anomalies, rigorous ablations, meticulous experiment logs, and clear communication of trade-offs
Ability to leverage AI tools (Copilot, ChatGPT, Claude) to maximize efficiency while fully understanding the underlying details of the code shipped
Strong independent thinking and debugging skills
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
Strong communication skills with the ability to write tidy PRs, experiment logs, and short design notes to ensure reproducibility

Nice To Haves

Experience with Python services (FastAPI/Flask), Docker, and AWS services (S3, Batch/EC2, ECR)

Responsibilities

Implement training loops, curate datasets, drive high-priority experiments, and partner with cross-functional teams to close feedback loops from edge cases
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