Technical Lead (Multimodal Transformers)

• 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
• 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

Benefits

• Remote-friendly work environment
• 401k
• Generous PTO policy
• Life insurance
• Assortment of medical (PPO/HMO/HDHP), dental, vision, and FSA plans
• Family-friendly company events
• Dog-friendly office
• Employee-driven fitness classes
• Beautiful, renovated facilities in Mountain View, CA and Lancaster, TX
• Free EV charging
• Trivia and board game nights- 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
• Strong command of cross-attention, token fusion, and modality alignment techniques
• Experience mentoring engineers and growing technical talent
• Passion for building AI that reasons over the full breadth of sensory input to operate safely in the real world
• Ability to influence technical direction across teams without formal authority
• Deep expertise in transformer architectures, particularly in multimodal or multi-stream settings
• Expert proficiency in Python and PyTorch, including large-scale distributed training and mixed-precision optimization