Machine Learning Engineer

Responsibilities

• Build agentic harnesses for target assessment, evidence synthesis, and experiment planning that allow models to provide guarantees about their processes.
• Develop data integrations across literature, scientific databases, customer data, and internal tools.
• Create APIs that customers can use in their own systems.
• Build evaluation systems that help understand whether changes improve user outcomes.
• Implement trust and transparency features, like source-quality signals, intermediate reasoning, and 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.

Requirements

• Strong software engineering background and ability to 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 ability to turn fuzzy user problems into concrete things people can use.
• Excitement to solve difficult, creative problems rather than narrow optimization on well-defined benchmarks.
• Ability to move across backend, data, and model layers as needed.
• Clear communication with product, design, domain experts, and other engineers.
• Ability to use coding assistants effectively and thoughtfully.

Compensation

• Career (L3): $185-230K + equity
• Senior (L4): $230-260K + equity
• Expert/Staff (L5): $255-340K + significant equity
• Targeting senior-level (L4) or above.