K
Kinetic
Senior Machine Learning Engineer
Skills
DockerFlaskPythonPyTorchAmazon EC2Machine LearningAWSFastAPI
2 months ago
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Important information
Contract type:
Permanent
Location:
Costa Mesa, CA, USA
Work mode:
Onsite
Published on:
29 August 2026
What they need
- 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
- 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- Strong communication skills with the ability to write tidy PRs, experiment logs, and short design notes to ensure reproducibility
- 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
- Proficiency in Python and the relevant tech stack: PyTorch, torchvision, Detectron2 or MMDetection/Segmentation, and Hugging Face Transformers
- 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
- 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
- Experience with Python services (FastAPI/Flask), Docker, and AWS services (S3, Batch/EC2, ECR) is preferred
- 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
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