Technical Lead, Multimodal Research

The role

You will own the modeling strategy and technical execution for understanding video data at petabyte scale. This senior individual contributor role involves deciding on models, representations, and evaluation methods to power Physical AI training.

What you will do

• Own modeling strategy across the platform including model families, representations, and training approaches.
• Take approaches from prototype into production inference at corpus scale.
• Work with data systems and storage teams on index and loader requirements.
• Define evaluation standards, benchmarks, and acceptance criteria for models.
• Own the cost curve for understanding through architecture-level decisions on distillation, cascades, routing, and quantization.
• Translate customer research needs into scoped technical programs.
• Set the technical direction for multimodal work across the company.

Required qualifications

• 5+ years in applied computer vision or multimodal ML.
• PhD or MS in computer science, electrical engineering, robotics, or applied mathematics with a computer vision or machine learning focus, or a comparable publication or production record.
• Depth in modern vision and multimodal modeling including VLMs, VQA, embeddings, representation learning, detection, tracking, segmentation, and retrieval.
• Hands-on training and evaluation of models at scale on real video and sensor data.
• Comfort across the research and engineering boundary including PyTorch prototyping, inference performance, GPU utilization, throughput, and cost.
• Background from a perception or multimodal team at a self-driving, robotics, or Physical AI company, a frontier research lab, or a visual-data company.

Nice to have

• Publications at CVPR, ICCV, ECCV, NeurIPS, ICML, or ICLR.
• Built or fine-tuned VLMs or other multimodal foundation models.
• Long-form video, temporal reasoning, embeddings, retrieval, or content-aware indexing at scale.
• Multimodal sensor data beyond RGB — lidar, radar, depth, or simulation output.
• Evaluation frameworks, labeling taxonomies, or large-scale annotation programs, or inference and training optimization across large GPU clusters.

Benefits

• Competitive comp and meaningful startup equity.
• Catered lunches and dinners for SF employees.
• Commuter benefit.
• Team-building events and poker nights.
• Health, vision, and dental coverage.
• Flexible PTO.
• Latest Apple equipment.
• 401(k) plan with match.

About the company

Eventual is building data curation for Physical AI by processing video and sensor data at petabyte scale. The small team is from AWS, Lyft and Tesla, and is backed by Felicis, CRV, Y Combinator and the co-founders of Databricks and Perplexity.