You'll build a comprehensive system that runs fast, is easy to use, and supports quickly building new evals:
Speed: You’ll build a lightning-fast basic evals infrastructure that schedules tasks to introduce practically no latency; and then you’ll figure out clever ways to solve the fundamental sources of latency (building a version of Elicit, running it on a query, and evaluating it using LMs)
Interfaces: ML engineers need evals to kick off automatically on relevant commits, with results they can see at a glance and drill into. Product managers need dashboards showing performance over time and what's going wrong in production.
Architecture: Your code must be well-architected so other team members and ML engineers can understand and build on it. An engineer starting on a new feature should be able to quickly add examples and run an eval.
We need to evaluate how well Elicit actually helps with decision-making in pharma, not just measure what's easy to measure. This requires encoding real knowledge about how pharma customers make decisions (for example, choosing appropriate gold standards).
You'll provide appropriate statistical tests and confidence intervals so we can trust our results.
In a typical month, expect to spend:
60% working on the core eval platform
15% working closely with the evals team to build and improve specific evals (e.g., an eval of our paper search within our systematic review flow)
10% mentoring our evals engineering intern
The rest on learning how people interact with the eval system so you can make it work better for them, and understanding what our users want from Elicit so evals measure what matters

Requirements

At least 3 years of experience as a professional software engineer, with demonstrated experience building complex backend systems (e.g., backend for a complex website, data pipelines, etc.)
Aptitude and interest in evaluating how Elicit helps with pharma decision-making. There's no particular experience you must have, but we'll evaluate your aptitude.
Knowledge of statistics (for e.g. calculating power and credence intervals for evals)
Experience with advanced Python (asyncio/trio and parallel processing strategies)
Front-end experience and strong UX sensibility (you'll be building dashboards). TypeScript experience is a plus.
Experience building developer tools (ML engineers are one of your most important clients)
Previous experience as a data engineer or working on AI infrastructure
Knowledge of pharma/biomed
Experience evaluating ML systems
Experience building language-model-based systems (helps with understanding Elicit and how to evaluate it)

Benefits

Flexible work environment: work from our office in Oakland or remotely with time zone overlap (between GMT and GMT-8), as long as you can travel for in-person retreats and coworking events
Fully covered health, dental, vision, and life insurance for you, generous coverage for the rest of your family
Flexible vacation policy, with a minimum recommendation of 20 days/year + company holidays
401K with a 6% employer match
A new Mac + $1,000 budget to set up your workstation or home office in your first year, then $500 every year thereafter
$1,000 quarterly AI Experimentation & Learning budget, so you can freely experiment with new AI tools, take courses, purchase educational resources, or attend AI-focused conferences and events
A team administrative assistant who can help you with personal and work tasks

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

Pythonasynciotrioparallel processingstatisticsdata pipelinesbackend systemsdeveloper toolslanguage-model-based systemsevaluating ML systems

Soft Skills

mentoringcollaborationproblem-solvinguser experience sensibility