Member of Technical Staff, Specialized Focus

About Eventual

Every breakthrough AI application, from foundation models to autonomous vehicles, relies on processing massive volumes of images, video, and complex data. But today’s data platforms (like Databricks and Snowflake) are built on top of tools made for spreadsheet-like analytics, not the petabytes of multimodal data that power AI. As a result, teams waste months on brittle infrastructure instead of conducting research and building their core product.

Every breakthrough AI application, from foundation models to autonomous vehicles, relies on processing massive volumes of images, video, and complex data. But today’s data platforms (like Databricks and Snowflake) are built on top of tools made for spreadsheet-like analytics, not the petabytes of multimodal data that power AI. As a result, teams waste months on brittle infrastructure instead of conducting research and building their core product.

Eventual was founded in 2022 to solve this. Our mission is to make querying any kind of data, images, video, audio, text, as intuitive as working with tables, and powerful enough to scale to production workloads. Our open-source engine, Daft, is purpose-built for real-world AI systems: coordinating with external APIs, managing GPU clusters, and handling failures that traditional engines can’t. Daft already powers critical workloads at companies like Amazon, Mobileye, Together AI, and Cloud Kitchens.

We’ve assembled a world‑class team from Databricks, AWS, Nvidia, Pinecone, Git Hub Copilot, Tesla, and more, quadrupling our size within a year. With Series A and seed funding from Felicis, CRV, Microsoft M12, Citi, Essence, Y Combinator, Caffeinated Capital, Array.vc, and top angels from the co‑founders of Databricks and Perplexity, we’re looking to double the team now. Join us—Eventual is just getting started.

Please note we’re looking for individuals who are excited to be a part of a tight‑knit team working together 4 days / week in our SF Mission district office.

As a Member of Technical Staff, you will be responsible for building Eventual's core products and architecture. You will ship features that will be immediately used by our customers and will work with a tight‑knit team that values open communication and cross‑functional collaboration. We move quickly to solve a wide range of complex technical and product challenges. While we are an experienced team that can provide constant guidance and mentorship, we value engineers who can autonomously scope and solve difficult technical challenges.

• Design and build highly reliable and resilient products and features.
• Work closely with cross‑functional product and customer-facing teams to understand requirements and ship thoughtful solutions.
• Write high‑quality, extensible, and maintainable code.
• Design and build scalable applications and components.
• Design and build APIs to drive existing and new features for a web-based BYOC enterprise product.

We are looking for strong engineers who are problem‑solvers at heart—combining excellent coding and architectural fundamentals in languages like Rust, C++, Python, or Go with a drive to reach for lower‑level primitives when performance and efficiency demand it. We look for engineers with a technical spike in at least one of our specialized focus areas, though expertise across all of them is not required.

Areas that are highly relevant to the work that we do at Eventual:

• Production ML/AI Deployment:
  • Experience building and training state‑of‑the‑art models (LLMs, VLMs, VLAs, CV models, world models). Bonus points for distributed training experience.
  • Familiarity with inference optimization (batching, GPU utilisation, TensorRT/ONNX Runtime, streaming data loaders) to minimise latency and maximise throughput.
  • Deep experience with inference frameworks (VLLM, SGLang) or building your own
• Large‑Scale Distributed Systems & Query Engines:
  • Hands‑on experience building, scaling, or optimizing distributed data/analytical engines (e.g., Apache Spark, Big Query, Hadoop, Snowflake, Ray, or custom execution engines).
  • Understanding of distributed engine internals: query planning, logical/physical…