Machine Learning Engineer

Machine Learning Engineer

As a Machine Learning Engineer at Elicit, you'll build products and workflows that help researchers and scientific teams make higher quality decisions with language models.

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.

You'll work on problems like:

• Turning messy, ambiguous research tasks into clear product experiences
• Building interfaces and artifacts that help users understand, trust, and act on model outputs, thinking beyond the chat interface while leveraging full model capabilities
• Combining language models with external tools, structured and unstructured data, and retrieval systems
• Improving quality through building careful evaluations, truth-conducive model environments and tools, and targeted ML modeling where the impact is high

You'll build agentic harnesses for target assessment, evidence synthesis, and experiment planning that allow models to provide guarantees about their processes. You'll also create data integrations across literature, scientific databases, customer data, and internal tools. Additionally, you'll develop APIs that customers can use in their own systems and 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, are also part of your responsibilities.

Examples of projects you could work on include building a target-assessment workflow that combines literature, genetics, chemistry, clinical, regulatory, and company data into a shareable artifact. You could also build experiment-planning and iteration tools that help researchers decide what to do next and learn from new results. Other projects might involve building evidence-monitoring workflows that keep teams up to date through alerts, briefs, and living reports, as well as enterprise APIs and structured-output pipelines that plug Elicit into customers' internal systems. You'll also develop interfaces that make it easier to inspect, trust, and correct model outputs and workflow-specific evals and quality systems that tell us whether a product change actually helped users. Lastly, you'll improve extraction, reasoning, or search quality with better prompts, better system design, or finetuning when appropriate.

To get a sense for how some of us look at applications, see this thread. (The short version: Wherever we can, we prefer to directly evaluate work.)

You'll thrive here if you 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, want to build new kinds of software made possible by language models, and are excited to use AI tools as part of your daily engineering workflow, while still applying strong judgment.

What we're not looking for includes mainly focusing on low-level model systems work like CUDA optimization or model serving infrastructure, working only on research experiments without owning production systems, and optimizing benchmark numbers without much connection to user workflows or product outcomes.

Consider these questions to determine if you're a good fit:

1. How does a transformer work?
2. What is a tokenizer?
3. What is a decorator in Python?
4. What are generic types?

Strong applicants will find it easy to answer these questions.

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.

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):

• 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
• 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
• Every Elician receives 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
• $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!
• You can find more reasons to work with us in this thread.

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:

• 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.