Research Engineer, Multimodal Data
About the role
Role focused on building and deploying visual understanding capabilities for multimodal data at scale. Owns roadmap, model training/evaluation, data pipelines, and production-inference integration to enable corpus-scale annotation for video and sensor data.
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 the whole corpus" remains 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.
Qualifications
- Strong familiarity with modern vision and multimodal models — convolution nets, VLMs, VQA, embeddings — and a sense for deployable SOTA.
- 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 — have 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."
Skills
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.
About Eventual
Building the AI data engine for any modality and scale
Every breakthrough AI application, from foundation models to autonomous vehicles, relies on processing massive volumes of images, video, and complex data. But today’s data platforms (like Databricks and Snowflake) are built on top of tools made for spreadsheet-like analytics, not the petabytes of multimodal data that power AI. As a result, teams waste months on brittle infrastructure instead of conducting research and building their core product. Eventual was founded in 2022 to solve this. Our mission is to make querying any kind of data, images, video, audio, text, as intuitive as working with tables, and powerful enough to scale to production workloads. Our open-source engine, Daft, is purpose-built for real-world AI systems: coordinating with external APIs, managing GPU clusters, and handling failures that traditional engines can’t. Daft already powers critical workloads at companies like Amazon, Mobileye, Together AI, and CloudKitchens. We’ve assembled a world-class team from Databricks, AWS, Nvidia, Pinecone, GitHub Copilot, Tesla, and more, quadrupling our size within a year. With backing from Y Combinator, Caffeinated Capital, Array.vc, and top angels from the co-founders of Databricks and Perplexity, we’re looking to double the team now. Join us—Eventual is just getting started. Please note we are looking for someone who is willing and able to come into our San Francisco office in the Mission district 4 days / week.
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