Research Engineer, Multimodal Data

From humanoid robots to autonomous vehicles, every Physical AI model is trained on petabytes of video, lidar, radar, and sensor data. Today's data platforms (Databricks, Snowflake) were built for spreadsheet-like analytics, not video corpora. And understanding that video still means paying a person to watch it, ten dollars an hour of footage at the low end. So teams check a sample and hope it represents the rest. The footage grows every year; the budget to look at it doesn't.

Eventual was founded in 2022 to close that gap. Our open-source engine, Daft, is purpose-built for multimodal AI: 2 PB/day at Amazon, 60-100 PB at another FAANG company, and in production at companies like Mobileye, TogetherAI. On top of it we're building the infrastructure that finds any situation you can describe across a fleet's entire video history, and turns it into a training set or an alert someone can still act on. We fine-tune and run the vision models ourselves, which makes indexing every hour cheaper than annotating a sample.

We're building this with the top Physical AI labs and GPU cloud providers. We've raised $30M from investors like Felicis, CRV, Y Combinator, and angels from the co-founders of Databricks and Perplexity. Our team comes from AWS, Lyft, and Tesla. We powered the last generation of Physical AI in self-driving; now we're doing it for the next.

Join our small (but powerful!) team, 4 days/week in our SF Mission District office.

As a Research Engineer on the Visual Understanding team, you'll own the layer that makes petabytes of video queryable by content. Physical AI teams have video, lidar, radar, and sim outputs scattered across object stores with no way to find what they need without weeks of human annotation. Eventual runs vision/language models and pipelines over every clip in a corpus along axes the customer cares about (gripper type, failure mode, object class, scene, motion density), so a researcher can ask "left-arm grasp failures on deformable objects" and get a curated dataset in minutes.

You'll define the roadmap for our visual understanding capabilities, train and select the models that make corpus-scale annotation tractable at single-digit cents per hour of video, and build the rich datasets that go on to train customer models. This is a applied research role — meaning you'll read papers and run experiments, but you ship to production and your work has a real impact on our customers’ models and robots.

• 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 CNNs 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.

• 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 run on thousands of GPU-hours of video.

• Experience training and evaluating vision or multimodal models (not just calling APIs).

• ML/AI research background — papers, citations, or a research org on your resume.

• Worked on embeddings, retrieval, or content-aware search at scale.

• Experience designing labeling taxonomies or running annotation programs.

• 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.

If you're excited about being on the team that turns petabytes of raw video into the training data for the next generation of Physical AI, we'd love to talk.