Research Engineer, Multimodal Data

About the role

Join our small (but powerful!) team working together 4 days/week in our SF Mission district office. Your Role As a Research Engineer on the Visual Understanding team, you'll own the layer that makes petabytes of video queryable by content.

Key Responsibilities

• Own the visual understanding roadmap end-to-end: from picking the model family for a customer's taxonomy to landing it in production inference at corpus scale.
• Train, fine-tune, and evaluate VLMs, VQA models, embedding models, and convolutional perception models against customer datasets and benchmarks.
• Drive down per-clip annotation cost — model selection, distillation, batching, decode pipelining — so "annotate every clip in a 10K-hour corpus" stays economical.
• Build the rich, queryable datasets that customers train on: design taxonomies with researchers, instrument quality, version the outputs.
• Partner with the dataloading and storage teams so visual understanding outputs flow into the index and on to the GPU without re-engineering.
• Work directly with researchers at our partner labs — your shortest feedback loop is their next training iteration.

What we look for

• Strong familiarity with modern vision and multimodal models — convolution nets, VLMs, VQA, embeddings — and a sense for the SOTA that's actually deployable today vs. on a leaderboard.
• Experience running these models at scale on real video and sensor data, ideally for perception tasks (detection, tracking, segmentation, retrieval, captioning).
• Background from a perception team at a self-driving, robotics, or visual-data company — or equivalent depth from a research lab.
• Comfortable with cloud infrastructure and large-scale data processing — you don't need to be a distributed-systems engineer, but you've shipped jobs that ran on thousands of GPU-hours of video.
• Bias toward data and infrastructure: you reach for "annotate the whole corpus" before "fine-tune another model."
• Nice to have: Experience training vision or multimodal models from scratch (not just calling APIs). ML/AI research background — papers, citations, or a research org on your resume. Hands-on time with big-data frameworks like Spark, Ray, or Daft. Worked on embeddings, retrieval, or content-aware search at scale. Experience designing labeling taxonomies or running annotation programs.

Perks & Benefits

• In-person, tight-knit team — 4 days/week in our SF Mission office.
• 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.

Compensation Range

$150K - $250K