Data Engineer, Applied AI

Company Summary

Lila Sciences is a privately held, early-stage technology company pioneering the application of artificial intelligence to transform every aspect of the scientific method. Lila is backed by Flagship Pioneering, which brings the courage, long-term vision, and resources needed to realize unreasonable results. Join our mission-driven team and contribute to the future of science.

Our Life Sciences effort is leveraging AI and high-throughput automation for valuable therapeutic discovery and development across biological modalities.

At Lila, we are uniquely cross-functional and collaborative. We are actively reimagining the way teams work together and communicate. Therefore, we seek individuals with an inclusive mindset and a diversity of thought. Our teams thrive in unstructured and creative environments. All voices are heard because we know that experience comes in many forms, skills are transferable, and passion goes a long way.

If this sounds like an environment you’d love to work in, even if you only have some of the experience listed below, please apply.

The Role:

We are seeking a Data Engineer to join our Applied AI group and build the data pipelines and tools that power our AI-driven scientific platform. In this role, you will design, implement, and maintain large-scale data architectures, ensuring secure and efficient data flows for advanced machine learning and generative AI models. You will also collaborate closely with scientists, software engineers, and product managers to translate internal algorithms and diverse datasets into production-ready tools and knowledgebases. This is a unique opportunity to shape the foundation of our AI capabilities by leveraging distributed systems, AWS, and Kubernetes to deliver scalable, reliable solutions that drive our agentic AI systems.

Key Responsibilities:

• Data Pipeline Development: Design and implement robust, scalable data pipelines to support machine learning and generative AI workflows, including Retrieval-Augmented Generation (RAG).
• Distributed Systems: Architect and manage distributed data-processing systems that handle large volumes of structured and unstructured data in real time.
• Cloud & Infrastructure: Leverage AWS services (e.g., S3, EC2, Lambda, and others) to build highly available, fault-tolerant data solutions; utilize Kubernetes for container orchestration and scalability.
• Integration & Collaboration: Work cross-functionally with scientists, engineers, and product managers to define platform requirements, integrate new data sources, and ensure seamless data flow into AI/ML pipelines.
• Data Governance & Quality: Establish best practices for data security, compliance, and quality assurance, ensuring the reliability and integrity of all datasets used in production.
• Performance Optimization: Monitor and optimize data workflows for throughput, fault-tolerance, and cost efficiency; implement robust logging, monitoring, and alerting for production readiness.

Qualifications:

• Educational Background: Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
• Professional Experience: 3+ years of experience building and maintaining production-grade data pipelines or distributed systems.
• Proficiency in Python: Strong Python skills with a solid grasp of object-oriented programming principles and common data engineering libraries/frameworks.
• Relational Databases: Fluency in relational database usage (e.g., PostgreSQL) for schema design, query optimization, and data governance.
• AWS Expertise: Hands-on experience with AWS cloud services for data ingestion, storage, and processing; comfortable designing and deploying infrastructure-as-code solutions.
• Distributed Systems Knowledge: Demonstrated ability to implement and manage distributed data-processing systems (e.g., Spark, Kafka, or similar).
• Communication & Collaboration: Exceptional communication skills with the ability to explain complex technical concepts to both technical and non-technical stakeholders.

Preferred Qualifications:

• Experience with ML & Generative AI: Prior work on data pipelines specifically supporting ML or generative AI models; familiarity with the MLOps lifecycle.
• Retrieval-Augmented Generation (RAG): Hands-on experience with RAG techniques and knowledgebases for AI systems.
• Kubernetes Proficiency: Comfort with container orchestration and scaling using Kubernetes.
• Agentic AI Systems: Exposure to or experience building agent-driven platforms where AI systems autonomously execute complex tasks.
• Startup Environment: Experience adapting quickly and delivering results in a fast-paced, evolving environment.
• Domain Background: Exposure to life sciences, material sciences, or related fields.

More About Flagship Pioneering

Flagship Pioneering is a biotechnology company that invents and builds platform companies, each with the potential for multiple products that transform human health or sustainability. Since its launch in 2000, Flagship has originated and fostered more than 100 scientific ventures, resulting in more than $90 billion in aggregate value. Many of the companies Flagship has founded have addressed humanity’s most urgent challenges: vaccinating billions of people against COVID-19, curing intractable diseases, improving human health, preempting illness, and feeding the world by improving the resiliency and sustainability of agriculture.  Flagship has been recognized twice on FORTUNE’s “Change the World” list, an annual ranking of companies that have made a positive social and environmental impact through activities that are part of their core business strategies, and has been twice named to Fast Company’s annual list of the World’s Most Innovative Companies. Learn more about Flagship at www.flagshippioneering.com.

Flagship Pioneering and our ecosystem companies are 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.

At Flagship, we recognize there is no perfect candidate. If you have some of the experience listed above but not all, please apply anyway. Experience comes in many forms, skills are transferable, and passion goes a long way. We are dedicated to building diverse and inclusive teams and look forward to learning more about your unique background.

Recruitment & Staffing Agencies: Flagship Pioneering and its affiliated Flagship Lab companies (collectively, “FSP”) do not accept unsolicited resumes from any source other than candidates. The submission of unsolicited resumes by recruitment or staffing agencies to FSP or its employees is strictly prohibited unless contacted directly by Flagship Pioneering’s internal Talent Acquisition team. Any resume submitted by an agency in the absence of a signed agreement will automatically become the property of FSP, and FSP will not owe any referral or other fees with respect thereto.