Senior Machine Learning Engineer

kinetic.auto; Costa Mesa (CA); On-site; USD 90,000 - 130,000; Full time

Benefits offered by this job

Competitive salary and equity package

Comprehensive health and dental insurance

Retirement savings plan

Paid time off and holidays

Job summary

Kinetic.auto in Costa Mesa, California is seeking a Machine Learning Engineer to join their small, production-minded ML team. You will work on vision models for tasks like image segmentation and object detection. The ideal candidate has experience with deep ML principles, proficiency in Python and PyTorch, and is keen on delivering high-quality code. The role offers a competitive salary, equity package, and comprehensive health benefits, along with an inclusive work environment.

Qualifications

• Hands-on experience training and evaluating deep models for segmentation and detection.
• Understanding of Transformer/LLM building blocks related to vision models.
• Familiarity with point clouds and camera geometry.

Responsibilities

• Implement training loops and curate datasets.
• Collaborate with cross-functional teams on model development.
• Run experiments to track metrics and present findings.

Skills

Deep ML / CV Fundamentals

Curiosity & Strict Attention to Detail

AI-Empowered, Not AI-Dependent

Knowledge of transformer and LLM building blocks

Practical exposure to 3D/depth data

Proficiency in Python, PyTorch

Experience with Python services (FastAPI/Flask)

Strong communication skills

Tools

PyTorch

Detectron2

Hugging Face Transformers

Docker

AWS services (S3, EC2)

Job description

Kinetic Automation is building a network of automated repair centers for modern vehicles. The auto industry is transitioning from mechanically complex vehicles to mechanically simple ones with complex software and technology. Kinetic aims to be the primary infrastructure-as-a-service for servicing future vehicles with our robotic repair centers, powered by our proprietary software and AI. We are a strong team of experienced robotics + automotive + shared mobility enthusiasts who have worked in self-driving, mapping, lidar, motorsport, and ride-sharing. We are a venture backed startup (Series B) with a clear go-to-market strategy and meaningful revenue.

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

Kinetic is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. We do not discriminate based on race, color, religion, gender, gender expression, age, national origin, disability, marital status, sexual orientation, military status, or any protected attribute. 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.