Co-op, Software, Applied AI Platform Engineer
- Location
- San Francisco, CA USA
- Work mode
- On-site
- Employment type
- Full-time
Skills mentioned
- Python
- TypeScript
- REST APIs
- GraphQL
- SQL
- Data pipelines
- System design
- Server-side rendering (Next.js/Remix)
- LLM/agent frameworks (LangChain)
- OCR/document understanding
- Cloud infrastructure (AWS/GCP/Azure)
- Docker/Kubernetes
Picked out of the job description automatically; check the full description for what the employer asks for.
About the job
Your Impact at LILA
Lila Sciences builds AI systems that accelerate discovery across the physical and life sciences. The Platform Engineering team owns the core infrastructure and developer-facing services that power Lila's AI Science Factory™ — including APIs, data pipelines, and the agentic systems that orchestrate scientific workflows at scale.
This co-op has two focus tracks. Candidates may be matched to one or both based on interest and fit:
Track 1 — Data Platform: Build pipelines and services that structure scientific knowledge (literature, patents, lab data) into signals that power Lila's AI models and applications.
Track 2 — Agentic SSR (AI and Server-Side Rendering): Build the server-rendered, agentic layer of the Lila Platform — the infrastructure that enables AI agents to compose and serve dynamic scientific interfaces at runtime.
In either track, you will ship production code, work alongside research scientists and ML engineers, and contribute to a platform that runs real scientific experiments.
What You'll Be Building
Data Platform Track
- Build and maintain ingestion and transformation pipelines for scientific literature, patents, and lab-generated data
- Implement structured data extraction workflows, including schema design, validation, and quality evaluation
- Integrate retrieval and RAG pipelines that surface relevant documents, tables, and figures to downstream Lila applications
- Generate and validate structured datasets; run end-to-end pipelines and analyze results against clear evaluation criteria
- Collaborate with research scientists to translate scientific domain requirements into robust data models
- Document findings and present results to the team; contribute to a publication or open-source project where the work supports it
Agentic SSR Track
- Build server-side rendering services and APIs that enable AI agents to compose and deliver dynamic scientific interfaces
- Implement agentic workflow primitives: task routing, context management, streaming responses, and tool integration
- Develop platform features end-to-end: schema design, business logic, API surface, and automated tests
- Instrument code for observability; participate in debugging and root-cause analysis of production issues
- Improve developer experience through internal tooling, reusable libraries, and documentation
- Contribute to technical design discussions and code reviews
What You'll Need to Succeed
- Pursuing a Bachelor's, Master's, or PhD in Computer Science, Software Engineering, or a related field
- Strong foundation in software engineering fundamentals: data structures, algorithms, and system design
- Proficiency in Python and/or TypeScript
- Experience building or consuming REST or GraphQL APIs
- Familiarity with relational databases and SQL
- Ability to work independently, take ownership of tasks, and communicate progress clearly
- Curiosity about scientific data or AI-driven software systems and a willingness to learn quickly
Bonus Points For
Data Platform Track
- Familiarity with NLP, document understanding (OCR, table/figure extraction), or multimodal models
- Experience with LLM applications, embeddings, or retrieval systems (chunking, reranking, vector search)
- Experience building evaluation sets, annotation workflows, or working with messy real-world data
- Interest in scientific document parsing or knowledge graph construction
Agentic SSR Track
- Experience with server-side rendering frameworks (Next.js, Remix, or similar)
- Exposure to LLM APIs, agentic frameworks (LangChain, LangGraph, or similar), or AI-assisted developer tooling
- Experience with cloud infrastructure (AWS, GCP, or Azure) or containerized environments (Docker, Kubernetes)
- Familiarity with event-driven architectures or message queues (Kafka, Pub/Sub, SQS)
Both Tracks
- Experience contributing to or maintaining open-source projects
- Prior internship or co-op experience in a software engineering role
About LILA
Lila Sciences is building Scientific Superintelligence™ to solve humankind's greatest challenges. We believe science is the most inspiring frontier for AI. Rather than hard-coding expert knowledge into tools, LILA builds systems that can learn for themselves.
LILA combines advanced AI models with proprietary AI Science Factory™ instruments into an operating system for science that executes the entire scientific method autonomously, accelerating discovery at unprecedented speed, scale, and impact across medicine, materials, and energy. Learn more at www.lila.ai.
Guided by our core values of truth, trust, curiosity, grit, and velocity, we move with startup speed while tackling problems of historic importance. If this sounds like an environment you'd love to work in, even if you don't meet every qualification listed above, we encourage you to apply.
We’re All In
Lila Sciences is committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status.
Information you provide during your application process will be handled in accordance with our Candidate Privacy Policy .
A Note to Agencies
Lila Sciences does not accept unsolicited resumes from any source other than candidates. The submission of unsolicited resumes by recruitment or staffing agencies to Lila Sciences or its employees is strictly prohibited unless contacted directly by Lila Science’s internal Talent Acquisition team. Any resume submitted by an agency in the absence of a signed agreement will automatically become the property of Lila Sciences, and Lila Sciences will not owe any referral or other fees with respect thereto.
From Lila Sciences's careers page (Greenhouse), checked 9 Nov 2026.
How to apply
- Select Apply on lila.ai. It opens Lila Sciences's own application form in a new tab, run on Greenhouse.
- Fill in the form and upload your résumé there. Jobiwork never sees your application.
- Lila Sciences will reply to you directly. No real employer asks you for money to apply.
Posted 9 Nov 2026 · checked 9 Nov 2026 · Report this job