Machine Learning Engineer

Kinetic•Santa Ana, CA

82d

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

BS or MS in CS, EE, Math, or a related field with solid deep-learning coursework or projects (e.g., grad-level ML/CV/DL or equivalent independent work).
Hands-on experience training and evaluating deep models for segmentation and detection in PyTorch (or similar), including data prep, augmentations, losses, and metrics (IoU/AP).
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) is preferred.

Responsibilities

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.