Eventualcomputing

Eventualcomputing

Software Engineer, High Performance Computing

San Francisco

Sponsorship not specified$20k-$40kDetected 302 days ago
C++SnowflakeDatabricksAWSKubernetesPyTorchSystems EngineeringRoboticsSensorsCollaboration

About the role

  • Our job is to keep those GPUs fed: rank-aware sampling, NVMe caching, video and sensor co-loading, random access into clips, decode pipelining.
  • Streaming alone can already saturate a B200; the hard part is enabling the complex sampling patterns researchers actually need without giving up a single percentage point of MFU.
  • This is a systems engineering role for someone who feels physical pain when a system is slow.

Responsibilities

  • Design and build the video-native dataloader: rank-aware, NVMe-cached, random-access into clips, returns tensors directly to the GPU.
  • Profile and optimize the full data path from object store → NVMe → page cache → host RAM → device RAM. Eliminate every avoidable copy and stall.
  • Own performance benchmarks against customer baselines (custom DataLoaders, DALI, decord, LeRobot) and against our own historical numbers - regressions get caught at PR time.
  • Partner with researchers at our partner labs to land the loader in their training stack and measure MFU end-to-end.
  • As a Systems Engineer on the Dataloading team, you'll build the layer that turns multi-petabyte video corpora into dict[str, Tensor] already on the GPU at line rate.
  • Team-building events and poker nights.

Requirements

  • You can recite Jeff Dean's "numbers every programmer should know" in your sleep.
  • You reach for them when it matters and you know why.

Nice to have

  • Experience working with GPUs is a plus, but you don't need it on day one.
  • Experience working with SLURM, Kubernetes for GPU workloads, or other HPC schedulers.
  • Hands-on CUDA experience.
  • Deep expertise on memory and caching subsystems - page cache tuning, hugepages, NUMA pinning, GPU-Direct Storage.
  • Worked on video decode pipelines (PyAV, decord, NVDEC) or PyTorch DataLoader internals.
  • Contributed to open-source systems projects in Rust/C++.
  • In-person, tight-knit team - 4 days/week in our SF Mission office.
  • Catered lunches and dinners for SF employees.

Compensation

  • $20k-$40k

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.
  • Robotics and video-AI teams now lose 20-40% of their training time to dataloading alone.
  • 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 streams curated datasets to GPUs at line rate.
  • Saturates B200s today.
  • Aimed at NVL72 and Vera Rubin tomorrow.
  • 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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