Lilasciences

Staff Engineer, Data Platform

Cambridge, MA USA; San Francisco, CA USA · Staff+ · Full-time

Sponsorship not specified$192k-$272kDetected 29 days ago

PythonSQLNoSQLVector DatabasesAWSCloud PlatformsKubernetesMachine LearningAirflowLLMsRAGAI OrchestrationRecruitingResearchLeadershipMentoring

About the role

Comfortable working across structured, semi-structured, and unstructured data. - Proven track record of working cross-functionally with scientists, ML researchers, and engineers.
Your final offer will reflect your background, expertise, and expected impact.
USD salary ranges apply only to U.S.-based positions; international salaries are set to local market.

Design and evolve the core data infrastructure that ingests, stores, and serves data across scientific and ML workflows.
Make principled build-vs-buy decisions and establish architectural patterns adopted by the broader engineering organization.
Build reliable pipelines that bring in data from diverse sources: laboratory instruments, public scientific datasets, and external research literature.
Own the interfaces between upstream producers and downstream consumers.
Define and maintain data models, schema evolution practices, and data contracts that ensure consistency, discoverability, and long-term durability of scientific and platform data assets.
Partner with ML researchers, lab scientists, and product engineers to translate scientific and research requirements into platform capabilities.
Drive alignment on data standards and integration patterns across teams.
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.
Designed and shipped data platform components from the ground up, including ingestion frameworks, storage abstractions, and orchestration systems.
Experience building data infrastructure that serves agentic and LLM-driven workflows, including vector databases, RAG infrastructure, and retrieval-optimized data access patterns.

Bachelor's or Master's degree in Computer Science, Engineering, or a related field, and 8+ years as a software or data engineer with a focus on building and operating data infrastructure.
Production experience with relational and NoSQL databases, schema design, query optimization, and operational concerns at scale.
Experience with cloud infrastructure and containerized deployment (AWS, Kubernetes).
Hands-on experience with modern table formats and open lakehouse patterns (Iceberg, Delta Lake, Hudi).
Experience with workflow orchestration systems (Flyte, Airflow, Dagster, or similar).
Proficiency with AI-assisted development tools (Cursor, Claude Code, or similar) and ability to incorporate them effectively into day-to-day engineering work.

We offer competitive base compensation with bonus potential and generous early-stage equity. Your final offer will reflect your background, expertise, and expected impact.
International Benefits. Full-time employees outside the U.S. receive a comprehensive benefits program tailored to their region. USD salary ranges apply only to U.S.-based positions
Expected Base Salary Range
$192,000 - $272,000 USD

U.S. Benefits.
Full-time U.S. employees receive a comprehensive benefits program including medical, dental, and vision coverage
employer-paid life and disability insurance

raw outputs from laboratory instruments, experimental model results, curated public datasets, and the scientific literature that contextualizes all of it.
The data platform team is responsible for the infrastructure that moves, stores, transforms, and surfaces this data across the organization.
We are looking for a

al employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status

This listing is sourced directly from Lilasciences's careers page and normalized into a canonical job model.