Technical Lead, Multimodal Transformers

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 autonomy stack is built on AI that fuses diverse sensor streams into a unified, actionable understanding of the world. We are developing Giga Fusion Net , a large-scale multimodal transformer that learns rich, joint representations across camera, LiDAR, and radar through attention-based fusion. We are looking for a technical leader to own the architecture direction of this effort and grow the engineers building it.

This is a senior individual contributor role with significant scope. You will set technical direction for multimodal fusion at Kodiak, lead the workstream executing against it, and be accountable for the results landing on trucks.

In this role, you will:

• Own the architecture roadmap for multimodal transformers that fuse camera, LiDAR, and radar into unified representations
• Lead the project end to end: problem framing, experiment design, implementation, and production deployment
• Drive research direction on cross-modal attention, token fusion strategies, and efficient multi-stream tokenization, and make the calls on what gets built
• Set the technical bar through design reviews, code reviews, and architectural decisions on scalable training pipelines
• Mentor junior and mid-level engineers, and raise the level of research execution across the org
• Define the pretraining strategy, including self-supervised and contrastive objectives that learn transferable multimodal representations
• Partner with perception, planning, and infrastructure leads to align model design with system-level latency and compute budgets

What you’ll bring:

• PhD with 5+ years of industry experience, or MS/BS with 8+ years, in AI, Computer Science, or a related field
• Track record of leading multi-engineer technical efforts from research through production
• Deep expertise in transformer architectures, particularly in multimodal or multi-stream settings
• Strong command of cross-attention, token fusion, and modality alignment techniques
• Experience mentoring engineers and growing technical talent
• Expert proficiency in Python and PyTorch, including large-scale distributed training and mixed-precision optimization
• Ability to influence technical direction across teams without formal authority
• Passion for building AI that reasons over the full breadth of sensory input to operate safely in the real world

What we offer:

• Competitive compensation package including equity and annual bonuses
• Excellent Medical, Dental, and Vision plans through Kaiser Permanente, Cigna, and Met Life (including a medical plan with infertility benefits)
• Met Life 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
• Long Term Disability, Short Term Disability, Life Insurance
• 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
• Various incentive programs (referral bonuses, patent bonuses, etc.)

The pay range listed below reflects the base salary in 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…