Machine Learning Engineer

About the role

This is not a role for someone who only wants to develop models in isolation from user impact. A large part of the work is software engineering: building product experiences, APIs, data integrations, evaluation systems, and reliable harnesses that make language models reliably useful and trustworthy in high-stakes domains.

What you’ll build

• Agentic harnesses for target assessment, evidence synthesis, and experiment planning that allow models to provide guarantees about their processes
• Data integrations across literature, scientific databases, customer data, and internal tools
• APIs that customers can use in their own systems
• 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

Example projects

• Build a target-assessment workflow that combines literature, genetics, chemistry, clinical, regulatory, and company data into a shareable artifact
• Build experiment-planning and iteration tools that help researchers decide what to do next and learn from new results
• Build evidence-monitoring workflows that keep teams up to date through alerts, briefs, and living reports
• Build enterprise APIs and structured-output pipelines that plug Elicit into customers’ internal systems
• 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

What you bring

• 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
• A clear communication with product, design, domain experts, and other engineers
• The ability to use coding assistants effectively and thoughtfully, and has adapted their workflow to become much more effective with them

Where you’ll thrive

• Enjoying shipping user-facing things quickly
• Enjoying working on ambiguous problems with a lot of autonomy
• Care about product quality and user trust, not just technical novelty
• Wanting to build new kinds of software made possible by language models
• Excited to use AI tools as part of your daily engineering workflow, while still applying strong judgment

We’re not looking for

• Someone who mainly wants to do low-level model systems work like CUDA optimization or model serving infrastructure as their primary focus
• Someone who works only on research experiments without owning production systems
• Someone who optimizes benchmark numbers without much connection to user workflows or product outcomes

Compensation

We're targeting starting ranges of: Career (L3): $185-220K + equity, Senior (L4): $220-260K + equity, Expert/Staff (L5): $250-320K + significant equity. We're optimizing for a hire who can contribute at a L4/senior-level or above. We'd love to meet staff/principal level contributors as well. We also offer above-market equity for all roles at Elicit, as well as employee-friendly equity terms.

Location and travel

We have a great office in Oakland, CA, and we'd love to see you there if you're local. That said, we're just as happy for you to work remotely. We do get the whole team together for a quarterly retreat somewhere fun, because in-person time matters to us.

Benefits

• Flexible work environment - work from our office in Oakland or remotely as long as you can travel to work in-person for retreats and coworking events
• Full coverage of health, dental, vision, and life insurance for you, generous coverage for the rest of your family
• A flexible vacation policy, with a minimum recommendation of 20 days/year + company holidays
• A 401K with a 6% employer match
• A $200 monthly wellbeing stipend to spend on whatever supports your health and wellbeing
• A new Mac + $1,000 budget to set up your workstation or home office in your first year, then $500 every year thereafter
• A $1,000 quarterly AI Experimentation & Learning budget, so you can freely experiment with new AI tools to incorporate into your workflow, take courses, purchase educational resources, or attend AI-focused conferences and events
• A team administrative assistant that you can delegate personal and work tasks to
• Commuter benefits, a relocation bonus, and more!