Senior AI Infrastructure Engineer - Model Training

Kodiak; Mountain View (CA); On-site; USD 190,000 - 260,000; Full time

Benefits offered by this job

Equity

Annual bonus

Medical/Dental/Vision

Flexible PTO

Office perks: lunch/kitchen

Headspace/Calm services

401(k)

Commuter benefits

Job summary

Kodiak Robotics, Inc. in Mountain View, CA is seeking ML systems engineers to design and optimize high‑throughput data loading and distributed training infrastructure for autonomous vehicle AI workloads. You will enable multi‑node GPU clusters, mixed precision, and scalable dataset pipelines.

The role requires strong Python and PyTorch skills, plus systems-level experience in C++/CUDA/Triton. Competitive compensation and comprehensive benefits are offered.

Qualifications

• Experience with distributed training frameworks and techniques (PyTorch DDP/FSDP, DeepSpeed, Megatron, NCCL).
• Experience building high-performance data pipelines for large-scale training (streaming formats, sharding, storage/network-aware loading).
• Deep understanding of GPU performance: mixed precision, memory hierarchy, kernel fusion, profiling tools (Nsight, PyTorch Profiler).
• Strong Python skills and PyTorch internals; systems-level experience (C++/CUDA/Triton) a plus.
• Passion for scalable AI infrastructure for fast, reliable training.

Responsibilities

• Design high-throughput data loading and streaming systems for multimodal sensor data, including dataset formats, sharding, and prefetching pipelines that keep GPUs saturated.
• Build and optimize distributed training infrastructure across multi-node GPU clusters, applying various parallelism strategies.
• Maximize utilization of accelerators (BF16/FP8), memory optimization, fused kernels, and overlap of compute and communication.
• Profile end-to-end training pipelines to identify bottlenecks across storage, network, CPU preprocessing, and GPU compute.
• Develop scalable dataset construction pipelines converting raw driving logs into training-ready formats.
• Partner with ML teams to scale architectures from prototype to full-cluster training runs.

Skills

Distributed training

PyTorch DDP/FSDP

DeepSpeed

Megatron

NCCL

Data pipelines

Python

C++/CUDA/Triton

Education

BS/MS/PhD in CS or related field

Tools

WebDataset

MosaicML Streaming/MDS

NVLink

InfiniBand

Job description

Mountain View, CA

Kodiak Robotics, Inc. was founded in 2018 and has become a leader in autonomous ground transportation committed to a safer and more efficient future for all. The company has developed an artificial intelligence (AI) powered technology stack purpose-built for commercial trucking and the public sector. The company delivers freight daily for its customers across the southern United States using its autonomous technology. In 2024, Kodiak became the first known company to publicly announce delivering a driverless semi‑truck to a customer. Kodiak is also leveraging its commercial self-driving software to develop, test and deploy autonomous capabilities for the U.S. Department of Defense.

Kodiak's AI is only as good as the speed at which we can train it. Every improvement to our models – from GigaFusionNet to large-scale world models – depends on infrastructure that turns thousands of hours of multimodal driving data into training throughput. We are looking for engineers who make model training fast: streaming massive camera, LiDAR, and radar datasets without stalling a single GPU, sharding data and models efficiently across nodes, and extracting every FLOP from the latest hardware. If you measure your impact in tokens per second and GPU utilization, this role is for you.

In this role, you will:

• Design high-throughput data loading and streaming systems for multimodal sensor data (camera, LiDAR, radar), including dataset formats, sharding strategies, and prefetching pipelines that keep GPUs saturated
• Build and optimize distributed training infrastructure across multi-node GPU clusters, applying data, tensor, pipeline, and fully sharded (FSDP/ZeRO) parallelism to models that don't fit on a single device
• Maximize utilization of modern accelerators such as NVIDIA B200s through mixed-precision training (BF16/FP8), fused kernels, memory optimization, and communication/computation overlap
• Profile end-to-end training pipelines to find and eliminate bottlenecks across storage, network, CPU preprocessing, and GPU compute
• Develop scalable dataset construction pipelines that convert petabytes of raw driving logs into training‑ready, streamable formats
• Partner with ML teams to scale new architectures from prototype to full‑cluster training runs efficiently and reliably

What you’ll bring:

• BS, MS, or PhD in Computer Science or a related field, and at least 2-3 years of industry experience in ML systems or infrastructure
• Hands‑on experience with distributed training frameworks and techniques (PyTorch DDP/FSDP, DeepSpeed, Megatron, NCCL) and a strong grasp of parallelism trade‑offs
• Experience building high‑performance data pipelines for large‑scale training, including streaming dataset formats (WebDataset, MosaicML Streaming/MDS, or similar), sharding, and storage/network‑aware loading
• Deep understanding of GPU performance: mixed precision, memory hierarchy, kernel fusion, profiling tools (Nsight, PyTorch Profiler), and interconnects (NVLink, InfiniBand)
• Strong Python skills and proficiency in PyTorch internals; systems‑level experience (C++/CUDA/Triton) a plus
• Passion for building the infrastructure that lets AI for the physical world train faster, scale further, and improve continuously

What we offer:

• Competitive compensation package including equity and annual bonuses
• Excellent Medical, Dental, and Vision plans through Kaiser Permanente, Cigna, and MetLife (including a medical plan with infertility benefits)
• MetLife Legal Services, Identity & Fraud Protection, Hospital Indemnity Insurance, Accident Insurance, & Critical Illness Insurance
• Flexible PTO, 10 paid holidays, and generous parental leave policies
• Our office is centrally located in Mountain View, CA
• Office perks: dog-friendly, free catered lunch, a fully stocked kitchen, and free EV charging
• Wellbeing Benefits - Headspace through Cigna, Calm through Kaiser, One Medical, Gympass, Spring Health through Cigna, Rula (mental health navigation)
• Fidelity 401(k)
• Commuter, FSA, Dependent Care FSA, HSA

The pay range listed below reflects the base salaryin our SF/Silicon Valley location, across several internal levels. Actual starting pay will be based on job-related factors including: work location, experience, relevant training, education, skill level and performance during interview. Total compensation at Kodiak includes base pay, equity, bonus and a competitive benefits package

$190,000 - $260,000 USD

At Kodiak, we strive to build a diverse community working towards our common company goals in a safe and collaborative environment where harassment of any kind is strictly prohibited. Kodiak is committed to equal opportunity employment regardless of race, ethnicity, religion, gender identity, sexual orientation, age, disability, or veteran status, or any other basis protected by applicable law.

In alignment with its business operations, Kodiak adheres to all relevant statutes, regulations, and administrative prerequisites. Accordingly, roles that carry more sensitive requirements may be limited to candidates that can satisfy additional scrutiny and eligibility for such positions may hinge on verification of a candidate’s residence, U.S. person status, and/or citizenship status. Should the position require, and Kodiak determines that a candidate’s residence, U.S. person status, and/or citizenship status necessitate an export license, bar the candidate from the position, or otherwise fall under national security‑related restrictions, Kodiak will consider the candidate for alternative positions unaffected by such restrictions, under terms and conditions set forth at Kodiak’s sole discretion, or, as an alternative, opt not to proceed with the candidate’s application. If applicable, Kodiak may provide visa sponsorship for eligible candidates.