ROLE OVERVIEWBiotechnology is rewriting life as we know it, from the medicines we take, to the crops we grow, the materials we wear, and the household goods that we rely on every day. But moving at the new speed of science requires better technology. Benchling's mission is to unlock the power of biotechnology. The world's most innovative biotech companies use Benchling's R&D Cloud to power the development of breakthrough products and accelerate time to milestone and market. Come help us bring modern software to modern science.
Benchling is building Intelligence Engineering & Enablement, a small autonomous team within our Security & IT organization. We own three things: internal AI tooling, adoption, and AI-assisted workflows across the company; cross-functional and company-wide agentic AI applications that span departmental boundaries; and the source-of-truth datasets, pipelines, and analytics that all of the above depend on, in partnership with our Data, Analytics & Systems team. We span the bridge between departmental AI experimentation and enterprise-grade agentic systems in production - rapidly prototyping new solutions, and graduating proven prototypes into hardened, well-governed systems with full SDLC rigor.
We're built to be enablers. We set the patterns, standards, and shared infrastructure that let departmental teams and AI power users across the company build their own solutions, and we take on the agentic systems that no single team owns. It's early days for enterprise agentic AI at Benchling, and we'll be moving fast - iterating on prototypes, learning from internal customers, and changing direction as the field matures.
As the founding engineer for this team, you'll own the technical direction, architecture, and delivery of our agentic AI portfolio. You'll be a player-coach - hands-on most of the time, leading by doing - and partner closely with our AI Product Manager on prioritization and our Data, Analytics & Systems team peers on the data foundations that agentic systems depend on. This is a senior individual contributor role on a flat team: you'll lead the engineering team in ideation, planning, and delivery and you'll drive technical hiring, while people management responsibilities sit with the hiring manager.
Check out our engineering blog for examples of past work across Benchling.
RESPONSIBILITIES- Shape technical direction and architecture: Define the foundational architecture for enterprise agentic AI at Benchling - orchestration, agent frameworks, tool integrations (including MCP), memory and state management, evaluation, and observability. Make clear build vs. buy decisions across the stack with documented rationale.
- Build and ship the early portfolio yourself: Write production code at least half your time, particularly during the team's first year. Stand up the CI/CD, testing, evaluation, and deployment infrastructure for agentic systems - leveraging existing patterns from Benchling's Build organization wherever possible. Graduate prototypes from the AI Product Manager's discovery cycles into hardened, production-grade systems and own production support under a "you build it, you run it" model.
- Design for enterprise from day one: Build for multi-tenant isolation, secrets management, audit logging, payload encryption, role-based access controls, and human-in-the-loop controls calibrated to risk. Partner with Security Engineering on threat modeling for agentic architectures - prompt injection, tool misuse, data exfiltration vectors.
- Enable builders across the company: Coach power users and departmental teams on production patterns, develop the criteria that decide which prototypes graduate into enterprise-grade systems, and build the internal-facing developer experience - templates, SDKs, sandboxes - that lets builders outside this team ship safely.
- Partner across functions: Work closely with our Data, Analytics & Systems team peers on the source-of-truth datasets and pipelines that agentic systems depend on. Engage with department leaders on the workflows we're transforming, and with Benchling's platform and infrastructure teams to leverage existing capabilities rather than build parallel systems.
- Elevate engineering standards: Set the bar for code quality, testing and evaluation, documentation, and on-call practices. Drive technical hiring through interview loop design, bar-raising in interviews, and representing the team to senior candidates. Mentor engineers on the team and other AI builders across the company.
QUALIFICATIONS- 7+ years of professional software engineering experience building production systems, with strong systems design fundamentals.
- Hands-on experience building production systems that integrate with LLMs and/or agentic patterns: orchestration, tool use, memory and state management, evaluation, and observability.
- Demonstrated understanding of how to optimize workloads across deterministic and non-deterministic capabilities, striking the right architectural balance for the needs of the specific solution being implemented.
- Production experience with at least two of: Python, TypeScript/Node.js, Go; comfort with working across the stack.
- Hands-on expertise with LLM APIs (OpenAI, Anthropic), agentic frameworks (LangChain, CrewAI), RAG over business content (Confluence, contracts, policies), vector databases (pgvector, Pinecone), workflow automation (n8n, Langflow), and LLM observability and evaluation tooling (LangSmith, Arize).
- Track record of going from zero to one: a platform, function, or product area you built up from scratch and scaled.
- Experience operating in regulated or security-sensitive environments. Solid grasp of enterprise security fundamentals - encryption, access controls, audit logging, secrets management.
- Comfortable exercising technical leadership independent of positional authority. You set direction, raise the bar in design reviews, and grow other engineers through influence.
- Build software with a product-first approach. You ship code quickly and care about the real-world impact of your work.
- Enjoy ownership and building key pieces of platforms.
- Strong communication skills with both technical and non-technical audiences. You can translate department workflows into engineering plans, and engineering tradeoffs into business language.
- Interest in learning more about life science (prior knowledge is not required).
NICE TO HAVE- Background in enterprise SaaS, life sciences, or biotech.
- Familiarity with LLM orchestration patterns and frameworks (LangGraph, MCP, agent SDKs from major model providers).
- Experience with async orchestration (Temporal, Prefect, Airflow) applied to long-running or agentic workflows.
- Familiarity with SOC 2, HIPAA, or GxP compliance as they apply to AI systems.
- Experience building internal developer platforms or internal tools at scale.
- Direct experience coaching or enabling non-engineers (analysts, ops staff, business power users) to build with AI tooling.
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