Elicit

Machine Learning Engineer

Oakland, CA (or remote within US timezones) · Principal

Sponsorship not specifiedDetected 2006 days ago

PythonMachine LearningNLPA/B TestingResearchCommunication

About the role

Agentic harnesses for target assessment, evidence synthesis, and experiment planning that allow models to provide guarantees about their processes

building product experiences, APIs, data integrations, evaluation systems, and reliable harnesses that make language models reliably useful and trustworthy in high-stakes domains.
optimize benchmark numbers without much connection to user workflows or product outcomes
This is not a role for someone who only wants to develop models in isolation from user impact.
Build a target-assessment workflow that combines literature, genetics, chemistry, clinical, regulatory, and company data into a shareable artifact.
Build evidence-monitoring workflows that keep teams up to date through alerts, briefs, and living reports.
Build interfaces that make it easier to inspect, trust, and correct model outputs.
Build workflow-specific evals and quality systems that tell us whether a product change actually helped users.
Improve extraction, reasoning, or search quality with better prompts, better system design, or finetuning when appropriate.

Ability to move across backend, data, and model layers as needed
Ability to use coding assistants effectively and thoughtfully, and has adapted their workflow to become much more effective with them
A strong software engineering background and can build end-to-end systems, not just scripts or notebooks
Fluency with language models to reason well about prompting, retrieval, evals, failure modes, and where (and how) finetuning is or isn't worth it
Strong product sense and likes turning fuzzy user problems into concrete things people can use
An excitement to solve difficult, creative problems rather than narrow optimization on well-defined benchmarks
Clear communication with product, design, domain experts, and other engineers
To get a sense for how some of us look at applications, see this thread https://twitter.com/stuhlmueller/status/1704543826218729868. (The short version: Wherever we can, we prefer to directly evaluate work.)
Want to build new kinds of software made possible by language models
Are excited to use AI tools as part of your daily engineering workflow, while still applying strong judgment

Turning messy, ambiguous research tasks into clear product experiences
Combining language models with external tools, structured and unstructured data, and retrieval systems
Data integrations across literature, scientific databases, customer data, and internal tools
Evaluation systems that help us understand whether a change actually improves user outcomes
Trust and transparency features, like source-quality signals, intermediate reasoning, and better ways to inspect and fix outputs
Like shipping user-facing things quickly
Enjoy working on ambiguous problems with a lot of autonomy
Care about product quality and user trust, not just technical novelty

For all roles at Elicit, we use a data-backed compensation framework to make sure our salaries are market-competitive, equitable, and simple. For this role, we're targeting starting ranges of:

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
A new Mac + $1,000 budget to set up your workstation or home office in your first year, then $500 every year thereafter
Commuter benefits, a relocation bonus, and more!

Elicit https://elicit.com is building the reasoning layer for science and decision-making.
We use language models to search over 125 million papers, extract data, and surface insights so that researchers, policy-makers, and industry leaders can go from questions to evidence-backed decisions in minutes.
Today, hundreds of thousands of researchers have used Elicit to speed up literature reviews, automate systematic reviews, and explore new domains.
As we expand our impact beyond academic research, we are laying the groundwork for ML systems that are systematic, transparent, and unbounded https://blog.elicit.com/ai-safety/ when reasoning at scale.
To do this, Elicit is pioneering supervision of process, not outcomes https://ought.org/updates/2022-04-06-process.
Instead of favoring large black-box models, we break complex questions down into human-legible steps and supervise the reasoning process itself.
This approach delivers more transparent, defensible answers today and charts a safer path toward advanced AI tomorrow.
APIs that customers can use in their own systems
Build enterprise APIs and structured-output pipelines that plug Elicit into customers' internal systems.
For all roles at Elicit, we use a data-backed compensation framework to make sure our salaries are market-competitive, equitable, and simple.
In addition to working on important problems as part of a happy, productive, and positive team, we also offer great benefits (with some variation based on work location):

This listing is sourced directly from Elicit's careers page and normalized into a canonical job model.