Series A ($120M) | AI Data Platform
About Us
We are building the data infrastructure that powers the next generation of AI systems. Having recently closed a $120 Million Series A, we're scaling our team rapidly across engineering disciplines. We're looking for Staff-level engineers who can set technical direction, mentor teams, and operate with high ownership in a fast-moving, high-leverage environment.
We're hiring Staff Engineers across five tracks: Fullstack, Infrastructure, ML/AI, DevOps, and Security. Below are role-specific descriptions — candidates may indicate which track(s) best fit their background.
What You'll Do (All Tracks)
- Operate as a technical leader: architect systems end to end, set technical direction for your area, and ensure delivery of reliable, scalable, enterprise-grade systems.
- Partner cross-functionally with product, design, and other engineering teams to shape roadmap and technical strategy.
- Mentor and uplevel engineers across the org; help establish engineering culture, standards, and best practices.
- Drive projects from design through production, balancing speed with long-term maintainability.
- Contribute to a high-trust, high-autonomy environment — we manage our own infrastructure as code and expect deep ownership of the systems you build.
Staff Fullstack Engineer
- Design and build user-facing applications and the APIs/services that power them.
- Own features end to end — from data model and backend services to frontend UX.
- Collaborate closely with design and product to deliver polished, performant experiences.
- Establish patterns for frontend architecture, component libraries, and API design.
- Architect and lead core data platform components across ingestion, processing, storage, and serving layers at petabyte scale.
- Design event-driven and batch data pipelines (e.g., Kafka, Spark, Airflow, dbt) supporting AI training and inference workloads.
- Implement data governance, lineage, quality, and observability frameworks for audit-ready, compliance-grade pipelines.
- Optimize for cost, performance, and reliability across multi-tenant cloud environments (AWS/GCP).
- Design and build infrastructure and tooling that supports model training, fine-tuning, evaluation, and serving at scale.
- Partner with research and product teams to productionize ML/LLM-based features (e.g., intelligent ranking, automated analysis pipelines).
- Build robust feedback loops between model outputs and data pipelines to support continuous improvement.
- Evaluate and integrate new ML frameworks, vector stores, and serving infrastructure as the field evolves.
- Own CI/CD pipelines, deployment infrastructure, and developer tooling across the org (e.g., Kubernetes, Argo, Terraform).
- Build and maintain monitoring, alerting, and incident response systems (e.g., Datadog, PagerDuty).
- Drive infrastructure-as-code practices and improve developer velocity through tooling and automation.
- Partner with all engineering teams to ensure scalable, observable, and cost-efficient infrastructure.
- Define and implement security architecture across cloud infrastructure, data pipelines, and applications.
- Lead efforts on data privacy, compliance (e.g., SOC 2, HIPAA — relevant given sensitive data handling), and access control.
- Conduct security reviews, threat modeling, and incident response for production systems.
- Partner with engineering teams to embed security best practices into the development lifecycle.
- 8+ years of professional software engineering experience, with demonstrated Staff-level scope (architecture ownership, cross-team influence, mentorship).
- Track record of building and scaling systems in production at meaningful scale (data volume, traffic, or org size).
- Strong communication skills — able to drive technical decisions across teams and articulate tradeoffs to both technical and non-technical stakeholders.
- Comfort operating in ambiguity at an early-stage, fast-growing company.
- Track-specific technical depth (e.g., distributed data systems for Infra; ML serving/training infra for ML/AI; Kubernetes/IaC for DevOps; cloud security frameworks for Security; modern frontend/backend stacks for Fullstack).
- Experience in regulated industries (healthcare, finance) and familiarity with compliance-grade data handling.
- Experience scaling teams — hiring, mentoring, and establishing engineering culture at an early-stage company.
- Familiarity with LLM-based tooling and AI-assisted data workflows.
- Competitive salary and equity in a well-funded, high-growth Series A company.
- High-impact role with direct influence on technical direction and company trajectory.
- Offices in San Francisco and New York City, with remote work available for most roles.
