Data Engineer, Applied AI

As a Data Engineer in Lila Sciences' Applied AI group, your main responsibilities involve designing and implementing robust data pipelines that support machine learning and AI workflows. You will also manage distributed data-processing systems and collaborate with cross-functional teams to ensure seamless data integration. Your role is crucial in maintaining data governance and quality while optimizing performance for data workflows.

To apply for the Data Engineer role at Lila Sciences, you should have a Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field, along with a minimum of 3 years of experience in building and maintaining production-grade data pipelines. Proficiency in Python and relational databases like PostgreSQL, as well as hands-on experience with AWS services and distributed systems, are also essential.

The Data Engineer at Lila Sciences plays a critical role in AI projects by building and maintaining data pipelines that are foundational for machine learning models and generative AI systems. Your efforts will directly impact the efficiency and capability of AI-driven scientific research, enabling the teams to translate complex algorithms into actionable insights and solutions.

At Lila Sciences, you can expect a collaborative and inclusive team culture that values diverse perspectives and creative problem-solving. You'll work in a dynamic environment where your input is welcomed, and team members are encouraged to innovate and support one another. The culture thrives on collaboration between scientists, engineers, and product managers, creating a rich ground for learning and professional growth.

As a Data Engineer at Lila Sciences, you will leverage a variety of technologies, including AWS services like S3, EC2, and Lambda, as well as container orchestration tools like Kubernetes. You will also work with distributed data-processing systems such as Spark and Kafka, making use of your Python skills and knowledge of machine learning frameworks to build scalable, reliable data solutions.

When responding, focus on specific projects you've worked on. Discuss the design process, the technologies used, and how you addressed challenges during development. Highlight your role in ensuring the data pipelines were scalable and efficient.

Discuss your familiarity with tools like Apache Spark, Kafka, or similar frameworks. Explain your preference based on factors such as ease of use, scalability, or specific project requirements you've encountered.

Highlight your understanding of data governance practices. Discuss methodologies you’ve implemented to validate and clean data, ensuring high standards for quality and reliability in production systems.

Emphasize your communication skills and your approach to explaining complex concepts simply. Provide examples of how you've worked with scientists or product managers to define requirements and integrate their feedback effectively.

Prepare a specific example that showcases your ability to analyze performance and implement optimizations. Discuss the metrics you improved and the overall impact on the workflow efficiency.

Talk about specific projects where you've utilized AWS services. Detail how you leveraged these technologies to enhance data storage, processing, or reliability, and mention any certifications or training you may have completed.

Discuss your experience with Kubernetes in deploying and managing containers. Provide examples of projects where Kubernetes played a key role in scaling applications and maintaining uptime.

Mention specific blogs, podcasts, or conferences you follow to keep your skills sharp. Emphasize your commitment to continual learning and how you apply new trends or technologies in your work.

Highlight any experience you have with RAG and its importance in AI applications. Discuss how you would incorporate RAG into data pipelines, emphasizing the synergy between retrieval and generative tasks.

Share your thoughts on Lila Sciences’ mission to transform scientific research with AI. Discuss how you align with the company's values and the unique opportunities you see in contributing to innovative projects in a collaborative environment.