Senior Applied AI Engineer - Multimodal Transformers

Benefits

Parental leave

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Non-birth-parent leave

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Family-building benefits

• Fertility benefits: Not verified
• Adoption assistance: Not verified
• Surrogacy assistance: Not verified

Mental health support

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Relocation assistance

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Childcare support

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Learning budget

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Verification

Not verified checked Jun 7, 2026

Salary

$200K-$260K From the posting source

401(k) match

Reported from DOL Form 5500 industry filing (not employer-specific)

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Market context

U.S. role benchmark (BLS OEWS)

$111,944 U.S. median for this role

Projected growth (BLS Employment Projections)

+13.7% - Much faster than average

105% above the BLS role benchmark for data and ml aggregate.

Matched to SOC 15-1252 - Data and ML aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Role

Role function

Data From the posting source

Seniority

Senior From the posting source

Schedule

Shift type

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Weekend work

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Company

Equity

Offered Verified - SEC 10-K source

Application

Cover letter

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Assessment

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Deadline

Not stated

Where they hire

State eligibility is not yet verified.

About this role

Senior Applied AI Engineer - Multimodal Transformers San Francisco Bay Area 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 GigaFusionNet - a large-scale multimodal transformer that learns rich, joint representations across camera, LiDAR, and radar through attention-based fusion. We are looking for engineers to push the boundaries of how transformer architectures combine and reason over heterogeneous sensor data.This role is open to all levels - from those eager to contribute to cutting-edge research to experts driving innovation at scale. In this role, you will: Design and develop multimodal transformer architectures that fuse camera, LiDAR, and radar into unified representations Research and implement cross-modal attention mechanisms, token fusion strategies, and efficient multi-stream tokenization Build scalable training pipelines for large-scale multimodal transformers across massive real-world datasets Explore self-supervised and contrastive pretraining objectives that learn transferable multimodal

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