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 a performance engineer who specializes in making large-scale machine learning workloads fast and cost-efficient in the datacenter. This role is focused on distributed training runs spanning many nodes, and high-throughput batch inference sweeping petabytes of real-world autonomy logs for auto-labeling, data mining, ground-truth generation, and evaluation.
The optimization target here is not tail latency on a vehicle - it is throughput, cluster goodput, and cost per unit of data processed. A training run that wastes 30% of its GPU-hours on stalled data loaders, or an offline inference sweep that takes a week instead of a day, directly slows down how fast the whole company can iterate. You will own the gap between what our fleet of accelerators is theoretically capable of and what our workloads actually achieve: profiling across the stack, finding where the compute and the wall-clock time actually go, and closing the difference.
You will work at the intersection of accelerators, ML frameworks, and large-scale data infrastructure, partnering with the teams who own each layer to land wins that show up in training time-to-result and offline processing cost. At Applied, we encourage all engineers to take ownership over technical and product decisions, closely interact with users to collect feedback, and contribute to a thoughtful, dynamic team culture.
At Applied, you will:
Profile and optimize distributed training end to end - data loading and preprocessing, augmentation, kernel execution, gradient communication, and checkpointing
Optimize large-scale offline and batch inference over petabyte-scale sensor logs: batching and scheduling strategies, quantization and low-precision execution, graph optimization, and accelerator saturation across long-running sweeps
Establish roofline and performance models for our workloads, quantify the gap between achieved and theoretical performance, and stack-rank optimization opportunities by impact and effort
Improve multi-node scaling efficiency: sharding and parallelism strategies, collective communication, interconnect utilization, and memory-bandwidth and kernel-fusion bottlenecks
Drive cluster goodput - reduce GPU idle time from input pipeline stalls, storage and network I/O, scheduling gaps, stragglers, and failure recovery on long-running jobs
Build the benchmarking, observability, and regression-detection tooling that keeps performance from silently degrading as models and code evolve
Collaborate with engineers across functions to solve complex data and compute problems at scale
Contribute to a team culture that values effective collaboration, technical excellence, and innovation
We're looking for someone who has:
Hands-on ML performance engineering experience: profiling, roofline analysis, throughput optimization, and root-cause investigation in production systems
Experience with distributed multi-node training at scale (FSDP, DeepSpeed, Megatron, NCCL, or equivalent), including diagnosing scaling inefficiency as node count grows
Deep familiarity with GPU or accelerator performance concepts - memory bandwidth, kernel launch overhead, occupancy, quantization, collective communication
Experience with high-throughput or batch inference systems (NVIDIA Triton Inference Server, TensorRT, ONNX Runtime, Ray, or similar)
Fluency in Python and proficiency in C++ or another systems language
Excellent debugging, analytical, and problem-solving skills
A deep understanding of machine learning foundations, and the ability to develop technical solutions for problems with no established playbook
Nice to have:
GPU kernel development experience: CUDA, Triton, CUTLASS, or hand-tuned attention implementations
Experience with profiling toolchains such as Nsight Systems/Compute, PyTorch Profiler, or perf
Experience with GPU scheduling and orchestration on Kubernetes, Slurm, or Ray, including multi-tenant cluster utilization
Experience with fault tolerance and elastic training for long-running jobs - checkpointing strategy, straggler mitigation, preemption recovery
Familiarity with autonomy or robotics data (ROS, OpenCV, multi-sensor log formats)
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.
