Senior Software Engineer - ML Infrastructure
AI summary of the role
Design and implement distributed cloud GPU training approaches for deep learning model training and evaluation
What you’ll do
- 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
What you’ll bring
- 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
- Opinions about building a company-wide platform for ML training, evaluation, and deployment
Technologies
GPU · distributed training · PyTorch · TensorFlow · TorchServe · TensorFlow Serving · NVIDIA Triton inference server · Airflow · Flyte · cloud
About Applied Intuition
Builds the toolchain, vehicle OS and autonomy stacks that automakers, defense and off-road OEMs use to ship AI-driven vehicles.
Series F
Source and classification
Internal deployment & tooling · Evidence for this classification:
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
More from the job description
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 le [... source excerpt omitted ...] echnical 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 We're looking for someone who has: A Bachelor's degree in Computer Science, Soft [... source excerpt omitted ...] y 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 scope of the position. Please reference the job posting’s subtitle for where this position will be located. For pay transparency purposes, the base salary range for this full-time position in the location listed is: $153,000 - $222,000 USD annually. Don’t meet every single requirement? If you’re excited about this role but your past experience doesn’t align perfectly with every qualification in the job description, we encourage you to apply anyway. Yo
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