Full Stack Engineer, Scientific AI Models

Job description

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.

Benchling is the AI platform for biotech R&D. Scientists use Benchling to design experiments, capture structured data, and run AI agents and models directly in their workflows. Over 200,000 scientists around the world trust Benchling 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.

ROLE OVERVIEW

The Model Hub team is building a computational platform that provides access to cutting-edge scientific AI models to help scientists design better drugs. Scientific models (e.g. AlphaFold or Boltz2) predict structures, predict scientific properties, and generate new drug designs, acting as a design partner and a major time saver to scientists who are creating life-saving therapeutics.

As a full-stack engineer on the team, you’ll focus on building the scalable platform, APIs, and user interfaces for these models. Projects you might work on include: adding new models as soon as they're published, improving model performance and scalability, and enabling scientists to automate their in-silico workflows by chaining models together into pipelines. It’s early days for scientific AI models, both at Benchling and in the industry at large. We’ll rapidly iterate with customers and change directions quickly, figuring out new patterns for how we develop and go to market. We’ll win if we stay curious and obsess over our customers.

RESPONSIBILITIES

• Build and scale our scientific model platform by designing and implementing infrastructure for model inference that is fast, reliable, and cost-effective across a diverse set of scientific models.

• Create intuitive and beautiful UI and API interfaces for our computational users to execute high-throughput runs and analyze their results.

• Work directly with customers to ideate use cases, gather feedback, and onboard scientific teams.

• Shape how we build AI at Benchling, driving experimentation, contributing to technical direction, and helping evolve our engineering and product development practices as the field matures. 

QUALIFICATIONS

• 3+ years of experience with full-stack development

• Collaborative mindset, able to work closely with engineers, product managers, and scientists to bring new ideas to life.

• Desire to work in a fast-paced environment, where priorities can shift and rapid experimentation is encouraged. 

HOW WE WORK

This is an in-person team in San Francisco built around collaborating in the office in a fast-paced environment. We’re in the office Monday through Friday.

#LI-KW1

Benchling welcomes everyone.

We believe diversity enriches our team so we hire people with a wide range of identities, backgrounds, and experiences.

We are an equal opportunity employer. That means we don’t discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status. We also consider for employment qualified applicants with arrest and conviction records, consistent with applicable federal, state and local law, including but not limited to the San Francisco Fair Chance Ordinance.