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 full-time 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. This in-office expectation does not apply to contractor positions

About the role

The Axion Data team at Applied Intuition is building the data engine to train perception models. We build the means to run models at the edge such as Automatic Target Recognition (ATR) models, then backhaul data to our cloud pipeline that ingests and manages this data to enable the continual improvement of these models.

As a data engineer experienced in machine learning, you will be responsible for both our edge application which runs perception stacks and our cloud data engine which ingests video data to run the next iteration of the model before we deploy it back down to the edge. This includes optimizing our ability to run these models on both the edge and the cloud, as well as building MLOps tooling to gain insight and visibility into the pipeline for our stakeholders.

At Applied Intuition, you will

Construct optimized data pipelines to run ML models

Evolve our data engine architecture to scale high-fidelity labels, reduce annotation costs, and accelerate ML iteration cycles

Integrate foundation models (LLMs, VLMs, and multimodal models) to automate and enhance labeling, quality assurance, and data discovery

Leverage software-in-the-loop and hardware-in-the-loop testing

Interact with the DoD customer to understand their use cases, requirements, and triage needs during field events to deliver a superior customer experience

We're looking for someone who has

5+ years of relevant work experience

Bachelor Degree in Computer Science or relevant field

Familiarity with modern ML infrastructure, data-centric AI approaches and running large-scale jobs on GPUs

Created or worked on microservices and/or databases for data-oriented software

A hunger to learn and grow into a position of ownership and impact on a new product team

U.S. citizenship (legally required) and eligibility to obtain a security clearance

Nice to have

Full-stack experience React, TypeScript Python, Golang or similar

Experience with Docker, Kubernetes, Opensearch and Postgres

Direct experience with foundation models, including LLMs and VLMs, for data automation tasks

Background in autonomous driving or robotics perception

Experience with active learning, auto-labeling, or human-in-the-loop ML systems

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.