Eventualcomputing

Eventualcomputing

Research Engineer, Multimodal Data

San Francisco

Sponsorship not specified$60k-$100kDetected 83 days ago
SnowflakeDatabricksAWSCloud PlatformsMachine LearningSparkComputer VisionSystems EngineeringRoboticsSensors5G/LTEResearch

About the role

  • Physical AI teams have raw footage, lidar, radar, and sim outputs scattered across object stores with no way to find what they need without weeks of human annotation.
  • This is a research engineering role - meaning you'll read papers and run experiments, but you ship to production and your work is judged by what it does for customer training runs.

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.
  • Drive down per-clip annotation cost - model selection, distillation, batching, decode pipelining - so "annotate every clip in a 10K-hour corpus" stays economical.
  • 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.
  • As a Research Engineer on the Visual Understanding team, you'll own the layer that makes petabytes of video queryable by content.
  • Team-building events and poker nights.

Nice to have

  • 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.

Compensation

  • $60k-$100k

Benefits

  • Competitive comp and meaningful startup equity.
  • Commuter benefit.
  • Health, vision, and dental coverage.
  • 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 training vision or multimodal models from scratch (not just calling APIs).

Company info

  • Every breakthrough Physical AI system - humanoid robots, autonomous vehicles, video generation models - is trained on petabytes of video, lidar, radar, and sensor data.
  • But today's data platforms (Databricks, Snowflake) were built for spreadsheet-like analytics, not the multimodal corpora that power AI.
  • As a result, robotics and video-AI teams iterate on model improvement about once a week.
  • Most of that week isn't training - it's finding the right data: writing CV heuristics over raw footage, paying annotators for edge cases, hand-curating clips before a cluster ever spins up.
  • GPU bandwidth has grown 2-3× per generation.
  • Storage and pipelines haven't.
  • The gap widens every year.
  • Eventual was founded in 2022 to close it.
  • Our open-source engine, Daft https://daft.ai/, is the distributed data engine purpose-built for multimodal AI - already running 2 PB/day at Amazon, 60-100 PB at another FAANG company, and in production at Mobileye, TogetherAI, and CloudKitchens.
  • We are building a video-native index on top of our engine for Physical AI that collapses the data iteration loop.
  • Describe the dataset you want, get a curated table in minutes, feed it to your GPUs at line rate.
  • One iteration per day becomes the norm.
  • We're building this in partnership with the top PhysicalAI labs and public AI infrastructure companies today.
  • We have raised $30M from Felicis, CRV, Microsoft M12, Citi, Essence, Y Combinator, Caffeinated Capital, Array.vc http://Array.vc, and angels from the co-founders of Databricks and Perplexity.
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