Eventualcomputing

Eventualcomputing

Software Engineer, New Grad

San Francisco · Internship

Sponsorship not specified$60k-$100kDetected 309 days ago
PythonRustC++Distributed SystemsSnowflakeDatabricksAWSGCPAzureMachine LearningComputer VisionLLMsRoboticsSensorsResearchMentoring

About the role

  • If you're a new grad and the thought of spending the next two years bolting another LLM into another wrapper makes you tired, read on.
  • We move petabytes of video through GPU clusters.
  • We argue about io_uring and Parquet footers.

Responsibilities

  • Profile, benchmark, and optimize real systems on real workloads - petabytes of customer video on real GPU clusters.
  • Team-building events and poker nights.

Requirements

  • A real love for systems, distributed systems, databases, or data infrastructure - the kind of love that shows up in side projects, course projects you took further than required, or open-source contributions.

Nice to have

  • Built something obsessive in undergrad: a database, a query engine, a compiler, a custom decoder, a kernel module, a distributed system, an emulator, a graphics engine, a research project.
  • Open-source contributions, especially to systems projects.
  • Internship experience at a systems-heavy team - databases, ML infrastructure, GPU/HPC, storage, networking.
  • Familiarity with cloud technologies (AWS, GCP, Azure).
  • Background in computer architecture, OS, compilers, or distributed systems.
  • 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.
  • Bonus points if you've reached for the lower-level languages because the problem demanded it.

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.
  • We've assembled a world-class team from AWS, Render, Pinecone and Tesla.
  • We have spent our careers powering the last generation of PhysicalAI in self-driving, and are excited to now do this for the next.
  • Join our small (but powerful!) team working together 4 days/week in our SF Mission district office.
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