Data Engineer

About Elicit Elicit is an AI research assistant that uses language models to help researchers figure out what's true and make better decisions, starting with common research tasks like literature review. What we're aiming for: Elicit radically increases the amount of good reasoning in the world. For experts, Elicit pushes the frontier forward. For non-experts, Elicit makes good reasoning more affordable. People who don't have the tools, expertise, time, or mental energy to make well-reasoned decisions on their own can do so with Elicit. Elicit is a scalable ML system based on human-understandable task decompositions, with supervision of process, not outcomes. This expands our collective understanding of safe AGI architectures. Visit our Twitter to learn more about how Elicit is helping researchers and making progress on our mission. Why we're hiring for this role Since launching the newest version of Elicit last fall, response has been strong. We introduced Elicit Plus, our monthly subscription plan, and added thousands of paying users in a matter of months as well as hundreds of thousands of new sign-ups. This has been energizing for our team, but we want to ship more useful functionality to our users even faster. Our academic research paper search pipeline is at the heart of Elicit's capabilities, and we need a skilled Data Engineer to take it to the next level. Our mission is to make Elicit the most complete and up-to-date database of scholarly sources. As we continue to add new data sources and expand our coverage of academic literature, we face challenges in efficiently processing, deduplicating, and indexing hundreds of millions of research papers. We're looking for someone who can architect and implement robust, scalable solutions to handle our growing data needs while maintaining high performance and data quality. Our tech stack Data pipeline: Python, Flyte, Spark Frontend: Next.js, TypeScript, and Tailwind Backend: Node and Python We like static type checking in Python and TypeScript All infrastructure runs in Kubernetes across a couple of clouds We use GitHub for code reviews and CI Am I a good fit? Consider the questions: How would you optimize a Spark job that's processing a large amount of data but running slowly? What are the differences between RDD, DataFrame, and Dataset in Spark? When would you use each? How does data partitioning work in distributed systems, and why is it important? How would you implement a data pipeline to handle regular updates from multiple academic paper sources, ensuring efficient deduplication? If you have a solid answer for these-without reference to documentation-then we should chat! What you'll bring to the role 5+ years of experience as a data engineer building core datasets and supporting business verticals with high data volumes Strong proficiency in Python (5+ years experience) Experience with architecting and optimizing large data pipelines with Spark Strong SQL skills, including understanding of aggregation functions, window functions, UDFs, self-joins, partitioning, and clustering approaches Experience with Parquet file formats and other columnar data storage formats Strong data quality management skills Ability to balance technical expertise with creative problem-solving Excited to interact with product/web app and help ship new features (e.g., real-time updates, advanced filtering options) Experience shipping scalable data solutions in the cloud (e.g., AWS, GCP, Azure), across multiple data stores and methodologies Nice to Have Familiarity with web crawling technologies and best practices for data extraction from websites Experience in developing deduplication processes for large datasets Hands-on experience with full-text extraction and processing from various document formats (PDF, HTML, XML, etc.) Experience working with academic research databases or scientific literature Familiarity with machine learning concepts and their application in search technologies Experience with distributed computing frameworks beyond Spark (e.g., Dask, Ray) Knowledge of academic publishing processes and metadata standards Hands-on experience with Airflow, DBT, or Hadoop What you'll do You'll own: Building and optimizing our academic research paper pipeline You'll architect and implement robust, scalable solutions to handle our growing data needs while maintaining high performance and data quality. You'll work on efficiently processing, deduplicating, and indexing hundreds of millions of research papers. Your goal will be to make Elicit the most complete and up-to-date database of scholarly sources. Enhancing Elicit's data infrastructure You'll optimize our Spark jobs and data pipelines to handle large amounts of data efficiently. You'll implement data partitioning strategies in our distributed systems to improve performance. You'll develop processes to handle regular updates from multiple academic paper sources, ensuring efficient deduplication. Maintaining and improving data quality You'll implement robust data quality management processes to ensure the accuracy and reliability of our academic database. You'll work on developing defenses against unexpected changes from publishers to maintain data integrity. Your first week: Start building foundational context Get to know your team, our stack (including Python, Flyte, and Spark), and the product roadmap. Familiarize yourself with our current data pipeline architecture and identify areas for potential improvement. Make your first contribution to Elicit Complete your first Linear issue related to our data pipeline or academic paper processing. Have a PR merged into our monorepo, demonstrating your understanding of our development workflow. Gain understanding of our CI/CD pipeline, monitoring, and logging tools specific to our data infrastructure. Your first month: You'll complete your first multi-issue project Tackle a significant data pipeline optimization or enhancement project. Collaborate with the team to implement improvements in our academic paper processing workflow. You're actively improving the team Contribute to regular team meetings and hack days, sharing insights from your data engineering expertise. Add documentation or diagrams explaining our data pipeline architecture and best practices. Suggest improvements to our data processing and storage methodologies. Your first quarter: You're flying solo Independently implement significant enhancements to our data pipeline, improving efficiency and scalability. Make impactful decisions regarding our data architecture and processing strategies. You've developed an area of expertise Become the go-to resource for questions related to our academic paper processing pipeline and data infrastructure. Lead discussions on optimizing our data storage and retrieval processes for academic literature. You actively research and improve the product Propose and scope improvements to make Elicit more comprehensive and up-to-date in terms of scholarly sources. Identify and implement technical improvements to surpass competitors like Google Scholar in terms of coverage and data quality. Who you'll work with This role will report directly to James, our Head of Engineering, and work very closely with the rest of the engineering team: Luke (AI engineer, full-stack) Panda (AI engineer, infra) Justin (ML Engineer) You'll also spend a lot of time collaborating with Kevin (Head of Product), and co-founders Jungwon & Andreas. Compensation, benefits, and perks In addition to working on important problems as part of a productive and positive team, we also offer great benefits (with some variation based on location): Flexible work environment: work from our office in Oakland or remotely with time zone overlap (between GMT and GMT-8), as long as you can travel for in-person retreats and coworking events Fully covered health, dental, vision, and life insurance for you, generous coverage for the rest of your family Flexible vacation policy, with a minimum recommendation of 20 days/year + company holidays 401K with a 6% employer match $2,000 device budget to start, with more accumulating for each month of work $500 / year personal development budget A team administrative assistant who can help you with personal and work tasks You can find more reasons to work with us in this thread! For all roles at Elicit, we use a data-backed compensation framework to keep salaries market-competitive, equitable, and simple to understand. This role starts between $195-230K + equity, depending on your level. We're optimizing for a hire who can contribute at a L4/senior-level or above.

Created: 2024-11-05