How to apply for Senior Software Engineer

About Elicit

Elicit is a public benefit company building an AI research assistant that uses language models to help researchers figure out what is true and make better decisions. It has over 2 million users, and its subscription tiers from Plus to Enterprise are used daily by scientists and by teams at top pharma and life sciences companies. The product is a scalable ML system based on human-understandable task decompositions, with supervision of process, not outcomes.

About the role

This senior software engineer role owns hard problems across the stack, ships new functionality quickly, and helps scale the infrastructure behind Elicit. You will work on agentic features, orchestrating LLMs over millions of papers, and keeping latency low without losing the rigor users trust. The role is remote in the US and full-time.

A typical day

A typical day might involve writing code across the stack to ship a new agentic feature, reviewing LLM orchestration pipelines over millions of papers, and debugging latency issues without sacrificing rigor. You would also coordinate with others to scale infrastructure behind Elicit's subscription tiers used by scientists and pharma teams. The job details do not specify team rituals or meeting cadence, so ask about those in the interview.

Who Elicit is looking for

• Has senior-level experience owning problems across the full stack, from frontend to backend to infrastructure.
• Has built or scaled systems that orchestrate LLMs or other ML models over large datasets, such as millions of papers.
• Can ship new functionality quickly while maintaining low latency and high reliability for daily users at pharma and life sciences companies.
• Is comfortable working on agentic features and ML systems where the process is supervised, not just the outcomes.

Tips for this application

• In your resume or application, highlight specific projects where you shipped features fast and scaled infrastructure for a product with a large user base.
• Mention any experience with LLM orchestration, agentic features, or ML systems that process large document collections, since Elicit explicitly lists these as current work.
• Reference Elicit's public benefit status and its goal of expanding collective understanding of safe AGI architectures, but only if you can connect it to your own engineering work.
• Check Elicit's Twitter and website before applying to understand how the product is used by researchers and pharma teams, then use that context in your materials.
• Apply for the remote US location only if you are based in the US, since the job details specify Remote (US).

What to cover in your cover letter

Focus on your experience owning hard problems across the stack and shipping quickly. Describe a time you scaled infrastructure for an ML system or LLM-based product. Explain how you keep latency low without losing rigor. Connect your work to Elicit's goal of building a scalable ML system with supervision of process, not outcomes.

Research before applying

• Read Elicit's website and Twitter to understand how the AI research assistant works and how researchers use it for literature review.
• Look into Elicit's public benefit company status and what that means for its mission and product decisions.
• Study Elicit's stated goal of building a scalable ML system based on human-understandable task decompositions with supervision of process, not outcomes.
• Find out which pharma and life sciences companies use Elicit's Plus to Enterprise tiers and what those users need from the product.

Likely interview topics

Based on the job description, expect questions about:

• How you would orchestrate LLMs over millions of papers while keeping latency low.
• Your approach to building agentic features for a product moving from a tool people visit to an environment they live in.
• A specific example of scaling infrastructure for a product with millions of users or daily enterprise users.
• How you balance shipping new functionality quickly with maintaining the rigor that scientists and pharma teams trust.
• Your understanding of Elicit's task decomposition approach and supervision of process, not outcomes.

Common mistakes to avoid

• Submitting generic application materials that do not mention LLM orchestration, agentic features, or scaling ML systems over large document collections.
• Ignoring Elicit's specific focus on supervision of process, not outcomes, and treating it as a standard AI product role.
• Applying without checking that you are based in the US, since the role is Remote (US) and the company has a specific user base in pharma and life sciences.