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Lila Sciences

AI-powered scientific discovery for medicines

Finance Co-op

AI Application EngineeringConfirmed live in the last 24 hours
InternshipCambridge, MA, USANo salary listedIn-person

About the Job

Requirements

  • Hands-on experience using a large language model API, such as Anthropic or OpenAI, to build something beyond chat.
  • Finance or business coursework sufficient to partner credibly with FP&A, Procurement, and Accounting stakeholders.
  • Comfort ramping quickly on unfamiliar business software and integrating against it.
  • Strong written communication.
  • Ability to scope ambiguous problems into shippable prototypes.
  • Currently pursuing an undergraduate or master's degree.
  • Available for the full January to June term.

Responsibilities

  • Ramp quickly on the Finance team's existing technology stack across enterprise resource planning, FP&A, expense, and procurement systems.
  • Partner with FP&A, Procurement, Accounting, and Strategic Finance to identify high-value AI use cases.
  • Prototype tools using large language model APIs against Finance workflows and internal data sources from existing systems.
  • Measure time savings, accuracy gains, and adoption impact of each prototype.
  • Scope ambiguous problems into shippable proof-of-concept tools.
  • Document what works and capture best practices for broader Finance team adoption.
  • Support the rollout of successful pilots from prototype to team-wide use.

Desired Qualifications

  • Exposure to forecasting, variance analysis, or financial reporting workflows.
  • Prior hands-on use of enterprise resource planning, FP&A, expense, or procurement platforms.
  • Experience working with internal datasets or data pipelines.
  • Familiarity with evaluating and measuring AI tool impact.

About the Company

Lila Sciences

AI-powered scientific discovery for medicines

Lila.ai uses artificial intelligence to speed up scientific discovery in medicines and advanced materials. The system ingests large sets of scientific data, experimental results, and literature to identify patterns, generate hypotheses, and guide research programs. It differentiates itself by tackling deep scientific problems at scale and combining AI with domain expertise, rather than offering generic tools. The goal is to help humanity by delivering new medicines and materials that solve major biological and materials challenges through data-driven discovery.

Company size
501-1,000
Company stage
Series A
Total funding
$550M
Headquarters
Cambridge, MA
Founded
2024

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