Eventualcomputing

Eventualcomputing

Software Engineer, Product

San Francisco

Sponsorship not specified$60k-$100kDetected 341 days ago
PythonRustBackend DevelopmentFull-Stack DevelopmentSnowflakeDatabricksAWSCloud PlatformsMachine LearningData EngineeringComputer VisionRoboticsSensorsResearchCollaboration

About the role

  • We move fast, ship to production weekly, and care more about whether researchers are actually using the surface than how clean the abstraction is.

Responsibilities

  • Design and build the product UI for exploring, querying, and curating multimodal datasets - including video playback, clip-level annotation, and visualizations over corpus composition.
  • Build analytics that help researchers understand their corpus: distributions over labeled axes, dataset composition over time, query result quality, training-job dataset provenance.
  • Sit with researchers at design-partner labs, gather requirements directly, and turn them into shipped features in days - not quarters.
  • Proven track record of shipping core product features with strong user obsession, including direct collaboration with users to gather requirements, manage feedback, and provide timely support.
  • You'll work directly with researchers at our partner labs - your shortest feedback loop is them telling you what they wish they could see in their data.
  • Team-building events and poker nights.

Nice to have

  • Experience with video, image, or other multimodal content in the browser.
  • Background in developer-facing or technical products, especially for ML/AI or data engineering audiences.
  • Comfort with Python on the backend (our platform is Python/Rust).
  • Worked closely with research or technical end users before.
  • In-person, tight-knit team - 4 days/week in our SF Mission office.
  • Catered lunches and dinners for SF employees.
  • Latest Apple equipment.

Compensation

  • $60k-$100k

Benefits

  • Competitive comp and meaningful startup equity.
  • Commuter benefit.
  • Health, vision, and dental coverage.

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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