Applied Intuition is the vehicle intelligence company that accelerates the global adoption of safe, AI-driven machines. Founded in 2017 and now valued at $15 billion following its recent Series F funding round, Applied Intuition delivers the Vehicle OS, Self-Driving System, and toolchain to help customers build intelligent vehicles and shorten time to market. 18 of the top 20 global automakers and major programs across the Department of Defense trust Applied Intuition's solutions to deliver vehicle intelligence. Applied Intuition services the automotive, defense, trucking, construction, mining, and agriculture industries and is headquartered in Mountain View, CA, with offices in Washington, D.C., San Diego, CA, Ft. Walton Beach, FL, Ann Arbor, MI, 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. (Note: For EpiSci job openings, fully remote work will be considered by exception.)
We are looking for a software engineer with deep experience in optimizing ML models and deploying them on production-grade embedded runtime environments. You’ll work across the entire ML framework stack (e.g. PyTorch, JAX, ONNX, TensorRT, CUDA, XLA, Triton).
At Applied Intuition, you will:- Drive ML performance optimization on multiple technologies for on-road and off-road ADAS / AD stacks targeting deployment on a variety of embedded compute platforms
- Develop compute usage strategies to optimize efficiency and latency of model inference for compute boards selected by our customers
- Work on model pruning and quantization, and support deployment on memory constrained platforms
- Collaborate closely with ML engineers and software developers on technical efforts to find and optimize efficient model architecture solutions
- Set up methodologies to profile the model performance on target embedded compute platforms and identify performance bottlenecks as part of stack integration
- Bachelors in Electrical Engineering or Computer Science, OR B.Sc. in Computer Science, Mathematics, Physics or a related field
- 3+ years of experience with ML accelerators, GPU, CPU, SoC architecture and micro-architecture
- Strong software development skills with the focus on embedded programming
- Experience profiling and optimizing model performance on embedded compute platforms
- Experience in working with deep learning frameworks (e.g., PyTorch, JAX, ONNX, etc.)
- M.Sc or PhD in a ML related area
- Built an ML optimization framework from scratch before
- Deployed ML solutions to embedded chips for real time robotics applications
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 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: $159,053 - $199,295 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. You may be just the right candidate for this or other roles.
Applied Intuition is an equal opportunity employer and federal contractor or subcontractor. Consequently, the parties agree that, as applicable, they will abide by the requirements of 41 CFR 60-1.4(a), 41 CFR 60-300.5(a) and 41 CFR 60-741.5(a) and that these laws are incorporated herein by reference. These regulations prohibit discrimination against qualified individuals based on their status as protected veterans or individuals with disabilities, and prohibit discrimination against all individuals based on their race, color, religion, sex, sexual orientation, gender identity or national origin. These regulations require that covered prime contractors and subcontractors take affirmative action to employ and advance in employment individuals without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status or disability. The parties also agree that, as applicable, they will abide by the requirements of Executive Order 13496 (29 CFR Part 471, Appendix A to Subpart A), relating to the notice of employee rights under federal labor laws.
Top Skills
What We Do
As the foremost enabler of autonomous vehicle development, Applied Intuition equips engineering and product teams with software that makes it faster, safer, and easier to bring autonomy to market.
Applied’s suite of products, focused on simulation and analytics, delivers sophisticated infrastructure built for scale. Companies of all sizes use Applied to comprehensively test and rapidly accelerate their autonomous vehicle development.
Headquartered in Silicon Valley with offices in Detroit, Tokyo, and Munich, Applied is composed of software and automotive experts from the top companies in the world (such as Google, Amazon, Apple, Waymo, Tesla, Delphi, GM, and Bosch).
