---
title: 'Technical Lead, Multimodal Research at Eventual'
canonical: 'https://feeny.ai/job/technical-lead-multimodal-research-eventual-san-francisco-amzcr3my7a4a'
type: 'job'
last_seen: '2026-11-04'
---

# Technical Lead, Multimodal Research at Eventual

- **Company:** Eventual
- **Location:** San Francisco, CA
- **Employment:** full-time
- **Posted:** 2026-09-24
- **Last confirmed live:** 2026-11-04
- **Apply:** https://jobs.ashbyhq.com/eventualcomputing/a9a91934-70eb-43b3-9008-e401ed96dd94

## Job description

## About Eventual

Robots and world models learn about the physical world from video: millions of hours of people cooking, building, carrying and fixing things. That footage is piling up faster than anyone can review it, and much of it is mislabeled, out of sync or not useful for training. Most teams still choose what to train on by having people watch a small sample. When a model learns from bad examples, it learns the wrong behavior.

Eventual is building data curation for Physical AI. We process video and sensor data at petabyte scale and extract signals from it: how hands and objects interact, what happens over time (a failed grasp, a retry, a recovery) and whether a clip is reliable enough to learn from. Leading Physical AI labs and robotics teams use these signals to decide what goes into their next training run.

The work spans large-scale data systems, computer vision and ML research, and it ships directly into how frontier models get trained. We’re a small team from AWS, Lyft and Tesla, backed by $30M from Felicis, CRV, Y Combinator and the co-founders of Databricks and Perplexity. We helped power the last generation of Physical AI in self-driving, and now we’re building what the next generation trains on. Join our small (but powerful!) team, 4 days/week in our SF Mission District office.

## Your Role

As Technical Lead, Multimodal Research, you’ll own the execution of our technical vision behind everything Eventual can understand about a video. 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 decide which models, representations, and evaluation methods get us there, and prove them in production at petabyte scale, over hundreds of thousands of hours of video. This is a senior individual contributor role rather than a management one: you set research direction and make the architectural decisions, while staying hands-on with papers, models, and experiments.

## Key Responsibilities

- Own modeling strategy across the platform rather than one customer’s taxonomy: which model families, representations, and training approaches we invest in, which get prototyped, and when to move off one.
- Take approaches from prototype into production inference at corpus scale, working with the data systems and storage teams on what the index and the loader require.
- Define the evaluation standard — the benchmarks and acceptance criteria any model meets before it reaches a customer.
- Own the cost curve for understanding: architecture-level decisions on distillation, cascades, routing, and quantization that keep a 10K-hour corpus at single-digit cents per hour of video.
- Translate customer research needs into scoped technical programs — taxonomy, model plan, datasets, quality instrumentation — and set the technical direction for multimodal work across the company. No direct reports.

## What we look for

- 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. A comparable publication or production record is acceptable in place of the degree.
- Depth in modern vision and multimodal modeling — VLMs, VQA, embeddings, representation learning, detection, tracking, segmentation, retrieval — with judgment about what is deployable today rather than competitive on a leaderboard.
- Hands-on training and evaluation of these models at scale on real video and sensor data, and comfort across the research and engineering boundary: PyTorch prototyping alongside 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 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.

## 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.

## About Eventual

## Company Overview
- **One-liner**: Eventual builds an open-source, high-performance data engine (Daft) purpose-built for AI and multimodal workloads, making querying images, video, audio, and text as intuitive as working with tables.
- **Entity Type**: Private (Startup, Y Combinator W22 batch)
- **Headquarters**: San Francisco, California, USA (Mission District)
- **Founded**: 2022
- **Founders**: Sammy Sidhu (CEO) and Jay Chia

## Core Business
- **Primary Industry**: AI Infrastructure / Data Engineering / Multimodal Data Processing
- **Target Customers**: B2B, Enterprise AI teams, foundation model developers, autonomous vehicle companies, and any organization processing large-scale multimodal data (images, video, audio, text).
- **Mission / Purpose**: To make querying any kind of data—images, video, audio, text—as intuitive as working with tables, and powerful enough to scale to production AI workloads.

## Products & Services
- **Daft (Open-Source Engine)**: The company’s core product. A high-performance, distributed data engine designed for AI and multimodal data. It handles petabytes of data daily, coordinates with external APIs, manages GPU clusters, and handles failures that traditional engines (like Spark/Databricks) cannot. Available as an open-source Python library on GitHub (5,570+ stars).
- **Daft CLI**: A command-line tool for spinning up and managing Ray clusters for the Daft Query Engine.
- **Daft Examples & Benchmarking**: Public repositories providing usage examples and distributed query benchmarking tools.

## Market Standing
- **Valuation / Market Cap**: Not publicly disclosed.
- **Key Metric**: Total Funding – Backed by Y Combinator, Caffeinated Capital, Array.vc, and angel investors including the co-founders of Databricks and Perplexity. Specific funding amounts are not publicly disclosed.
- **Notable Investors / Partners**: Y Combinator (Winter 2022 batch), Caffeinated Capital, Array.vc. Key customers/partners include **Amazon**, **Mobileye**, **Together AI**, and **CloudKitchens**.
- **Growth Signals**: Quadrupled team size within the past year (from ~4 to ~18 employees). Daft already processes petabytes of data daily at major companies. Actively hiring to double the team again.

## Competitive Advantages
- **Purpose-Built for AI**: Unlike Databricks, Snowflake, or Spark, which were designed for tabular/analytics workloads, Daft is built from the ground up for multimodal AI data (images, video, audio, text).
- **Open-Source + Performance**: Daft is open-source (Apache 2.0), giving teams full control, while delivering performance competitive with proprietary engines. It handles GPU cluster orchestration and API coordination natively.
- **Founding Team & Advisors**: Founders have deep backgrounds in HPC, deep learning, self-driving cars (DeepScale/Tesla, Lyft L5), and ML infrastructure (Freenome). The team includes engineers from Databricks, AWS, Nvidia, Pinecone, and GitHub Copilot.
- **Traction with Top-Tier Customers**: Already deployed at Amazon, Mobileye, Together AI, and CloudKitchens, validating product-market fit with demanding enterprise workloads.

## Strategic Focus
- **Scale the Team**: Aggressively hiring across engineering (Product, Systems, HPC, Research) to double the current headcount of 18.
- **Expand Daft’s Capabilities**: Continue building out Daft’s multimodal query engine to handle even more complex AI workloads and modalities.
- **Deepen Enterprise Adoption**: Grow usage within existing customers (Amazon, etc.) and acquire new large-scale AI teams.
- **Community Growth**: Grow the open-source community around Daft (currently 5,570+ stars on GitHub).

## Why Work Here
- **High Impact**: Work directly on infrastructure that powers breakthrough AI applications (foundation models, autonomous vehicles). “Your work directly determines whether AI teams can build breakthrough applications or get stuck rebuilding infrastructure.”
- **World-Class Team**: Join a small, elite team of 18 engineers from Databricks, AWS, Nvidia, Pinecone, GitHub Copilot, and Tesla. “Quadrupling our size within a year” signals rapid scaling and opportunity.
- **In-Office Culture**: 4 days/week in the San Francisco Mission District office. Tight-knit, collaborative environment with catered lunch/dinner, poker nights, and team-building events.
- **Compensation & Benefits**: Competitive salary ($150K–$250K for engineering roles) + startup equity. Benefits include health/vision/dental, flexible PTO, 401k with match, commuter benefits, and latest Apple equipment.
- **Values-Driven**: “Take pride in our work,” “Create clarity,” “Be curious,” “Own the outcome.” Emphasis on writing high-quality code, tackling hard distributed systems problems, and direct ownership.
- **Growth Trajectory**: Early-stage (18 people) with strong traction and backing. Opportunity to shape the product and culture as the company scales.

## Sources
1. [Y Combinator – Eventual Company Profile](https://www.ycombinator.com/companies/eventual)
2. [Eventual Official Careers Page](https://www.eventual.ai/careers)
3. [Eventual GitHub Organization](https://github.com/eventual-inc)
4. [Y Combinator – Eventual Jobs Page](https://www.ycombinator.com/companies/eventual/jobs)
5. [Eventual Job Board (Ashby)](https://jobs.ashbyhq.com/eventualcomputing)

## Other roles at Eventual

- [Chief of Staff](https://feeny.ai/job/chief-of-staff-eventual-san-francisco-4nzq4g33xbb7) — San Francisco, CA
- [Software Engineer, Multimodal Backend Systems](https://feeny.ai/job/software-engineer-multimodal-backend-systems-eventual-san-francisco-szayste712fy) — San Francisco, CA
- [Software Engineer, Data Systems](https://feeny.ai/job/software-engineer-data-systems-eventual-san-francisco-je7sws8z9p6d) — San Francisco, CA
- [Research Engineer, Multimodal Data](https://feeny.ai/job/research-engineer-multimodal-data-eventual-san-francisco-q7myvdm4xpe4) — San Francisco, CA