Senior Software Engineer - ML Infrastructure

Applied Intuition•Sunnyvale, CA

9h•Onsite

About The Position

About Applied Intuition Applied Intuition, Inc. is powering the future of physical AI. Founded in 2017 and now valued at $15 billion, the Silicon Valley company is creating the digital infrastructure needed to bring intelligence to every moving machine on the planet. Applied Intuition services the automotive, defense, trucking, construction, mining and agriculture industries in three core areas: tools and infrastructure, operating systems, and autonomy. Eighteen of the top 20 global automakers, as well as the United States military and its allies, trust the company’s solutions to deliver physical intelligence. Applied Intuition is headquartered in Sunnyvale, California, with offices in Washington, D.C.; San Diego; Ft. Walton Beach, Florida; Ann Arbor, Michigan; London; Stuttgart; Munich; Stockholm; Bangalore; Seoul; and Tokyo. Learn more at applied.co . We are an in-office company, and our expectation is that employees primarily work from their Applied Intuition office 5 days a week. However, we also recognize the importance of flexibility and trust our employees to manage their schedules responsibly. This may include occasional remote work, starting the day with morning meetings from home before heading to the office, or leaving earlier when needed to accommodate family commitments. About the role We are looking for both infrastructure engineers with expertise in machine learning pipelines and ML engineers that want to work beyond modeling to join the Data & ML infra group. This role will work across the entire ML lifecycle (dataset generation, training frameworks, compute, evaluation, and deployment) and work directly with modeling teams. This team is a good fit if you are excited to work on broad, ambiguous problems and develop across the entire ML stack. At Applied Intuition, we encourage all engineers to take ownership over technical and product decisions, closely interact with external and internal users to collect feedback, and contribute to a thoughtful, dynamic team culture. At Applied Intuition, you will: Design and implement distributed cloud GPU training approaches for deep learning model training and evaluation Build end-to-end machine learning pipelines and integrate them into core product workflows Encourage change, especially in support of ML engineering best practices, and maintain a high standard of excellence Collaborate with engineers across the entire company to solve complex data problems at scale

Requirements

A Bachelor's degree in Computer Science, Software Engineering, or equivalent
3+ years of professional experience
Experience with building software components to address production, full-stack machine learning challenges. This is not purely a research problem
Opinions about building a company-wide platform for ML training, evaluation, and deployment
Knowledge of the open source landscape with judgment on when to choose open source versus build in-house
Excellent analytical and problem-solving skills

Nice To Haves

Experience with developing, running, and managing orchestration systems like Airflow and Flyte that non engineers can use to build data pipelines.
Experience with ML modeling frameworks (PyTorch, Tensorflow, etc.), and model serving platforms (TorchServe, TensorFlow Serving, NVIDIA Triton inference server, etc.)

Responsibilities

Design and implement distributed cloud GPU training approaches for deep learning model training and evaluation
Build end-to-end machine learning pipelines and integrate them into core product workflows
Encourage change, especially in support of ML engineering best practices, and maintain a high standard of excellence
Collaborate with engineers across the entire company to solve complex data problems at scale