Software Engineer, Tools and Automation

Benchling•San Francisco, CA

1d•$195,000 - $265,000•Hybrid

About The Position

Benchling is seeking a Software Engineer, Tools and Automation to join their AI & Data Engineering team. This role focuses on building internal AI capabilities and adoption, as well as the data and analytics infrastructure for the company. The engineer will work on prototyping new solutions, hardening them into production-grade systems, and supporting internal customers. This is an internal tools and automation role, acting as an internal forward-deployed engineer who collaborates directly with teams to automate their workflows. The primary focus is on building reliable internal systems and integrations, with AI being a supported area rather than the direct product. The founding engineer will own the technical direction, architecture, and delivery of internal tooling and automation, acting as a player-coach. The role involves hands-on coding, defining architecture, building CI/CD and testing infrastructure, designing for enterprise security, enabling other builders, and partnering across functions like data analytics and platform engineering. The position also involves elevating engineering standards, driving technical hiring, and mentoring engineers. Prior knowledge of life sciences is not required, but an interest in learning is encouraged.

Requirements

7+ years of professional software engineering experience building production systems, with strong systems design and integration fundamentals.
A track record of building internal tools, automation, and systems integrations that colleagues depended on daily - not customer-facing product work alone. You have wired together APIs, SaaS platforms, and internal services, and you owned the result in production.
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.
Practical experience applying LLMs where they earn their place in a workflow - and the judgment to recognize where they do not. We care that you shipped something that worked and that people kept using, not which framework you reached for.
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 non-technical audiences specifically. You can sit with a finance, legal, or sales ops team, understand their workflow well enough to challenge it, and come back with an engineering plan they recognize as their own problem - then explain the tradeoffs in their language.
Interest in learning more about life science (prior knowledge is not required).

Nice To Haves

Background in enterprise SaaS, life sciences, or biotech.
Familiarity with LLM orchestration and tool-integration patterns (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.
Model research or ML engineering.

Responsibilities

Shape technical direction and architecture: Define the foundational architecture for internal tooling and automation at Benchling - integration patterns across our SaaS estate, workflow orchestration, data access, service boundaries, and the observability that makes internal systems supportable. Where a workflow genuinely calls for an agent, own that architecture too: tool integration, state management, and evaluation. 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, and deployment infrastructure the team runs on - leveraging existing patterns from Benchling's Build organization wherever possible. Graduate departmental prototypes 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 the systems you build - including the agentic ones, where prompt injection, tool misuse, and data exfiltration are live concerns.
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 peers on the source-of-truth datasets and pipelines that agentic systems depend on. Sit down with department leaders and non-technical stakeholders, turn a vague "this process is painful" into a scoped and prioritized engineering problem, and drive it to a shipped system - largely on your own judgment. Work 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.