Head of Engineering

valency.io

valency.io

Software Engineering

Berkeley, CA, USA

Posted on May 19, 2026

Location

Berkeley, CA

Employment Type

Full time

Location Type

Hybrid

Department

Engineering & Technology

About Valency

Valency Systems is a small, dynamic team of engineers, scientists, and researchers building the global hub for the agentic research era.

We're based in Berkeley, California, and we're building something that matters. If you care about open science, advancing research at the speed of thought, and using AI to accelerate discovery, we'd love to talk.

Our team is hybrid. We come together in person 3 days a week, with the option for 2 days of remote work.

The Position

As our Head of Engineering, you will report directly to the CEO. You will own the technical architecture, define the engineering culture, hire and grow an engineering team, and shape how we build and ship.

You'll be hands-on, building your engineering organization from the ground up and leveraging the power of AI coding tools to accelerate innovation. You will work in tight collaboration with world-class peers in Head of Science and Head of Research. You will make sure we are building the right things, the right way, at the right speed.

If you thrive in a small, collaborative, fast-moving environments where technical decisions have immediate, visible impact on the product and the team, this role was built for you!

What You'll Do

Technical leadership & architecture

  • Own the technical roadmap, making architectural decisions that balance shipping speed with long-term scalability

  • Drive the evolution of our cloud-based infrastructure

  • Anticipate scaling and reliability challenges before they become expensive, staying ahead of the product to validate approaches and surface unknowns early

  • Establish engineering practices and standards that will scale as the team grows from a handful of engineers to a full organization

Hands-on engineering

  • Champion AI-tool fluency across the team, using tools like Claude Code as force multipliers

  • This is a building role, not a meetings role

  • Make key technical decisions across backend services, data infrastructure, and developer tooling

  • Drive measurable improvements in system performance, reliability, and operational cost

  • Build a robust infrastructure that will scale, as we serve the needs of the global scientific community

Cross-functional leadership

  • Be a true partner to the Head of Research and Head of Science — this is not a hand-off relationship, but tight collaborative loops

  • Work in close alignment with product to translate user needs and requirements into engineering priorities

  • Translate complex technical tradeoffs into clear, actionable guidance for technical and non-technical stakeholders alike

People & culture

  • Recruit, hire, and mentor a world-class engineering team across backend, frontend, infrastructure, and security

  • Build a culture where shipping fast (excellence), learning continuously (curiosity), and being good humans (kindness) can coexist

  • Foster an environment of intellectual humility, open source contribution, and AI-native development practices

What We're Looking For

Required

  • Significant experience building and leading engineering teams at early-stage or high-growth companies

  • Genuine fluency with AI coding tools: you already use them daily, to solve real problems, not just experimentally

  • Deep hands-on expertise with cloud-native architectures

  • Some experience with Model Context Protocol (MCP), agentic frameworks, or AI-tool integration patterns

  • A track record of scaling and shipping production systems under real constraints, not just designing them, but delivering them

  • Ability to operate with high autonomy in a fast-moving, resource-constrained environment

Preferred

  • Experience with our stack or adjacent: Node.js/TypeScript, AWS Lambda/CDK/Step Functions, PostgreSQL, Cloudflare

  • Experience scaling teams through key growth inflection points (e.g., 5 → 20 engineers)

  • Familiarity with ML infrastructure, vector search, or document processing pipelines (added bonus for experience in research/academia)

  • Familiarity with auth systems (e.g. OAuth) and observability tooling