СШАSoftware Engineer, Tools and Automation
1 мес. назад
195k–265k USD / годСШАMiddleHybrid
pythonautomationaws
Develop automation tools and software to support biotechnology research leveraging AI.
О компании
- We offer a flexible hybrid work arrangement that prioritizes in-office collaboration. Employees are expected to be on-site 3 days per week (Monday, Tuesday, and Thursday).
Обязанности
- Biotechnology 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. 's mission is to unlock the power of biotechnology. The world's most innovative biotech companies use '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.
- is building AI & Data Engineering, a small autonomous team within our Security & IT organization. We own two things: the internal AI capabilities and adoption so Benchlings can rapidly experiment and discover what works; and the source-of-truth data & analytics infrastructure the company depends on. 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. This is an internal tools and automation role - think of it as an internal forward-deployed engineer. You will sit with the teams whose work you are automating, scope the problem with them directly, and ship the system that fixes it. AI is what we are supporting, but not directly what we build: we want someone who builds reliable internal systems and integrations, and who reaches for a model when a model is genuinely the right answer. Model research or ML engineering experience is neither required nor the bar.
- 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 , 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 internal tooling and automation 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 & Science team peers on the data foundations that agentic systems depend on. This is an 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 .
- Shape technical direction and architecture: Define the foundational architecture for internal tooling and automation at - 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 '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 '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.
Требования
- 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).
Будет плюсом
- 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.
Другое
- We are rebuilding biotech for the AI era. When a breakthrough is delayed, the world waits. Getting a molecule from discovery to patients, or a crop from lab to field, involves thousands of slow, manual, disconnected steps. AI has the potential to change this, compressing decades of R&D work into years. But that only happens when clean, structured scientific data and AI are built into how science gets done. is the AI platform for biotech R&D. Scientists use to design experiments, capture structured data, and run AI agents and models directly in their workflows. Over 200,000 scientists around the world trust to power their most important work, from academic labs to Sanofi, Moderna, and more than half of the world's top 50 biopharma. We’re building an AI scientist for our customers. We can’t do that if we haven’t built the muscle ourselves. AI fluency is the foundation we build on; it's core to how we work, and we're committed to helping every new hire integrate it into their day-to-day. As part of our interview process, you'll complete a brief AI-focused exercise or discussion so we can understand how you think about and use AI to drive impact in your role. Feel free to reference any tools, platforms, or workflows you use today.
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