Co-op, Software, Applied AI Platform Engineer
Lila Sciences2 days ago
Responsibilities
- Build and maintain ingestion and transformation pipelines for scientific literature, patents, and laboratory data.
- Implement structured data extraction, schema design, validation, dataset generation, and quality evaluation workflows.
- Build retrieval and RAG pipelines for documents, tables, and figures.
- Develop server-side rendering services, APIs, and agentic workflow primitives such as task routing, context management, streaming responses, and tool integration.
- Develop platform features end-to-end with automated tests, observability, debugging, and root-cause analysis of production issues.
- Improve developer experience through internal tooling, reusable libraries, documentation, technical design discussions, and code reviews.
- Collaborate with research scientists and ML engineers, present findings, and potentially contribute to publications or open-source projects.
Requirements
- Pursuing a Bachelor's, Master's, or PhD in Computer Science, Software Engineering, or a related field.
- Strong foundation in software engineering fundamentals, including 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, communicate progress clearly, and learn quickly.
- Preferred qualifications include experience with NLP, OCR, table or figure extraction, multimodal models, LLM applications, embeddings, retrieval systems, evaluation sets, annotation workflows, scientific document parsing, knowledge graphs, server-side rendering, agentic frameworks, cloud infrastructure, containers, event-driven architectures, message queues, open-source projects, or prior software engineering internships or co-ops.
Tech Stack
Categories
AI ApplicationsData Engineering
About Lila Sciences
Lila Sciences builds an AI-driven research platform and autonomous lab systems for life science, chemistry, and materials R&D teams. Its products combine large AI models with robotic instruments to plan, run, and analyze experiments, integrating into customer-specific scientific workflows. The privately held company serves pharma, materials, and energy organizations and is venture-backed, including a Series A financing in 2025.