Engineering Manager - ML Platform and Infrastructure
개발 매니저정규직시니어 · 3년 이상
Applied Intuition에서 ML 플랫폼 및 인프라 엔지니어링 매니저를 채용합니다. 분산 시스템 및 대규모 GPU 클러스터 운영 경험이 필수이며, 물리적 AI를 위한 인프라 고도화를 주도합니다. 3년 이상의 관리 경험과 PyTorch, Kubernetes 활용 능력을 갖춘 분을 찾습니다. 세계 최고 수준의 엔지니어링 팀을 이끌며 기술적 난제를 해결할 기회입니다.
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.
As an Engineering Manager on the ML Platform team, you'll lead a world-class group of engineers focused on building the infrastructure that powers Physical AI at scale. Your team will own three critical areas: Training & Inference Orchestration, where we build frameworks to efficiently schedule and run massive jobs across thousands of GPUs; GPU Cluster Architecture, where we design and scale what will be the largest GPU cluster for Physical AI in the industry; and Performance Optimization, where we push the limits of hardware utilization, throughput, and cost efficiency for large-scale training and inference workloads. You'll work at the intersection of systems engineering and ML, partnering directly with stack development and research teams to remove bottlenecks and accelerate the path from experimentation to production.
Grow and manage a team of world-class infrastructure and systems engineers with the goal of delivering a best-in-class ML platform for Physical AI
Own the design and evolution of frameworks for orchestrating distributed training and inference jobs across thousands of GPUs
Drive the buildout and scaling of our GPU cluster infrastructure, making critical decisions on architecture, scheduling, networking, and resource management
Lead efforts to optimize training and inference performance — including throughput, fault tolerance, GPU utilization, and cost efficiency at scale
Set team goals and roadmap in alignment with research milestones, model development timelines, and production deployment requirements
Partner closely with research, stack development, and infrastructure teams to understand their workflows and accelerate their iteration speed
Drive hiring, mentoring, and growth for a high-performing, mission-driven team
3+ years of engineering management experience, ideally leading infrastructure or platform teams
Passion for building and leading high-performing teams that operate at the frontier of scale
Deep experience with distributed systems, GPU computing, or large-scale ML infrastructure
Direct experience building or operating large GPU clusters (1,000+ GPUs)
Strong understanding of distributed training frameworks (e.g., PyTorch Distributed, Megatron-LM, DeepSpeed, FSDP) and job orchestration at scale
Familiarity with GPU cluster management, high-performance networking (InfiniBand, RDMA), and resource scheduling (Slurm, Kubernetes)
Track record of building and operating systems that run reliably at massive scale
Background in training optimization techniques such as mixed-precision training, pipeline/tensor/data parallelism, or checkpointing strategies
Experience with inference optimization (batching, model serving, quantization, compiler-level optimizations)
Familiarity with Physical AI domains such as autonomous driving, robotics, or simulation
Contributions to open-source ML infrastructure projects
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: $204,000 - $343,000 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. 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.