Senior Software Engineer - ML Infrastructure

Senior Software Engineer - ML Infrastructure

Sunnyvale, California, United States

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  .

We are an in-office company, and our expectation is that employees primarily work from our 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.

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.

• 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

• 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

• 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 (Torch Serve, Tensor Flow Serving, NVIDIA Triton inference server, etc.)

Compensation at Applied Intuition for eligible roles includes base salary, equity, and benefits. Base salary is a single component of the total compensation package, which may also include equity in the form of options and/or restricted stock units, comprehensive health, dental, vision, life and disability insurance coverage, 401k retirement benefits with employer match, learning and wellness stipends, and paid time off.

Note that benefits are subject to change and may vary based on jurisdiction of employment.

Applied Intuition pay ranges reflect the minimum and maximum intended target base salary for new hire salaries for the position. The actual base salary offered to a successful candidate will additionally be influenced by a variety of factors including experience, credentials & certifications, educational attainment, skill level requirements, interview performance, and the level and…