Technical Lead, Multimodal Research

Eventual•San Francisco, CA

7d•Onsite

About The Position

Eventual is building the infrastructure for Physical AI, focusing on processing and understanding massive multimodal datasets (video, lidar, radar, sensor data). Current data platforms are inadequate for this scale and type of data, and manual annotation is prohibitively expensive. Eventual's open-source engine, Daft, is designed for multimodal AI, handling petabytes of data daily. The company aims to enable researchers to quickly find specific situations within vast video histories for training sets or alerts. They achieve this by fine-tuning and running vision models themselves, making indexing cheaper than sampling. Eventual has raised $30M and has a team with experience from leading tech companies in the autonomous vehicle and AI space.

Requirements

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/production record.
Depth in modern vision and multimodal modeling (VLMs, VQA, embeddings, representation learning, detection, tracking, segmentation, retrieval) with practical deployment judgment.
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, ideally as the senior-most person on that problem.

Nice To Haves

Publications at CVPR, ICCV, ECCV, NeurIPS, ICML, or ICLR.
Experience building or fine-tuning VLMs or other multimodal foundation models.
Experience with long-form video, temporal reasoning, embeddings, retrieval, or content-aware indexing at scale.
Experience with multimodal sensor data beyond RGB (lidar, radar, depth, or simulation output).
Experience with evaluation frameworks, labeling taxonomies, large-scale annotation programs, or inference and training optimization across large GPU clusters.

Responsibilities

Own modeling strategy across the platform, deciding on model families, representations, and training approaches.
Take approaches from prototype into production inference at corpus scale, collaborating with data systems and storage teams.
Define the evaluation standard, including benchmarks and acceptance criteria for models.
Own the cost curve for understanding by making architectural decisions on distillation, cascades, routing, and quantization.
Translate customer research needs into scoped technical programs, including taxonomy, model plans, datasets, and quality instrumentation.
Set the technical direction for multimodal work across the company.