Job Description
🌎 San Francisco (Hybrid)
💼 Founding/Staff Data Engineer
💵 $200-300k base
Our client is an elite applied AI research and product lab building AI-native systems for finance—and pushing frontier models into real production environments. Their work sits at the intersection of data, research, and high-stakes financial decision-making.
As the Founding Data Engineer, you will own the data platform that powers everything: models, experiments, and user-facing products relied on by demanding financial customers. You’ll make foundational architectural decisions, work directly with researchers and product engineers, and help define how data is built, trusted, and scaled from day one.
What you’ll do:
- Design and build the core data platform, ingesting, transforming, and serving large-scale financial and alternative datasets.
- Partner closely with researchers and ML engineers to ship production-grade data and feature pipelines that power cutting-edge models.
- Establish data quality, observability, lineage, and reproducibility across both experimentation and production workloads.
- Deploy and operate data services using Docker and Kubernetes in a modern cloud environment (AWS, GCP, or Azure).
- Make foundational choices on tooling, architecture, and best practices that will define how data works across the company.
- Continuously simplify and evolve systems—rewriting pipelines or infrastructure when it’s the right long-term decision.
Ideal candidate:
- Have owned or built high-performance data systems end-to-end, directly supporting production applications and ML models.
- Are strongest in backend and data infrastructure, with enough frontend literacy to integrate cleanly with web products when needed.
- Can design and evolve backend services and pipelines (Node.js or Python) to support new product features and research workflows.
- Are an expert in at least one statically typed language, with a strong bias toward type safety, correctness, and maintainable systems.
- Have deployed data workloads and services using Docker and Kubernetes on a major cloud provider.
- Are comfortable making hard calls—simplifying, refactoring, or rebuilding legacy pipelines when quality and scalability demand it.
- Use AI tools to accelerate your work, but rigorously review and validate AI-generated code, insisting on sound system design.
- Thrive in a high-bar, high-ownership environment with other exceptional engineers.
- Love deep technical problems in data infrastructure, distributed systems, and performance.
Nice to have:
- Experience working with financial data (market, risk, portfolio, transactional, or alternative datasets).
- Familiarity with ML infrastructure, such as feature stores, experiment tracking, or model serving systems.
- Background in a high-growth startup or a foundational infrastructure role.
Compensation & setup:
- Competitive salary and founder-level equity
- Hybrid role based in San Francisco, with close collaboration and significant ownership
- Small, elite team building core infrastructure with outsized impact