Machine Learning Engineer

THE POSITION
Our roster has an opening with your name on it

At FanDuel, data is the heartbeat of our organization.  As a Machine Learning Engineer at FanDuel, you will help us unlock the full potential of our vast amounts of real-time and relational data. You will be asked to provide our business with insight and our customers with world-class personalized experiences.  Every click our users make, every bet, every touchdown, every fumble, and every play is fair game for us to turn into a stream of knowledge. Your expertise will be used here to make better and faster decisions – outpacing our competition.

Collaboration is at the core of your role. You’ll be the linchpin between engineering teams working downstream to build out our online application and upstream to land necessary data for feature engineering. You’ll also be working with Data Scientists and Analysts to productionize, analyze, and validate AI powered insights. You will be asked to help organize, model, and present our data as a coherent product and offer it to our stakeholders, providing a common information framework that allows FanDuel to intelligently react to what is happening on the field and in the marketplace.   

We are looking for Machine Learning Engineer who may be looking to make the move to a big data environment.  If this describes you, read on – we want to hear from you!

In addition to the specific responsibilities outlined above, employees may be required to perform other such duties as assigned by the Company. This ensures operational flexibility and allows the Company to meet evolving business needs.

THE GAME PLAN
Everyone on our team has a part to play

• Designing and implementing intelligent search system incorporating typeahead search, vector search and ML personalization model signals to optimize relevance and user experience
• Contributing to the design and development of scalable serving systems for ML and GenAI/LLM models
• Developing platform features and capabilities (e.g. CLI, SDK, Infra Automation, Platform Applications) for streamlining ML Model and GenAI/LLM Application development and deployment lifecycle
• Business intelligence tools (e.g., Tableau, Knime, Looker)
• Data security and privacy (e.g. GDPR, CPP)
• Data governance and data testing frameworks
• Continuous integration and delivery of production data products
• An inclusive culture that expects excellence and priorities your growth as an engineer and your well-being as a person
• Advance your career within well-defined, skill-based tracks, either as an individual contributor or as a manager – both providing equal opportunities for compensation and advancement
• Collaborating with peers and sharing best practices in system reliability, automation, and data quality

ML engineering is a rapidly changing field – most of all, we’re looking for someone who enjoys experimenting, keeping their finger on the pulse of current data engineering tools, and always thinking about how to do something better.

THE STATS
What we're looking for in our next teammate

• 3-5+ Years of relevant experience developing code in one or more core programming languages (Python, Java, etc.)
• Experience implementing vector search, semantic search, or embedding-based retrieval systems for production ML or AI applications
• Experience working with typeahead / autocomplete systems and integrating ML signals into query understanding or ranking workflows
• Experience combining outputs from multiple retrieval systems (e.g., vector search + typeahead + personalization models) to improve relevance
• Hands-on experience in deploying ML and GenAI/LLM models under the constraints of scalability, correctness, and maintainability.
  • Hands on experience with ML frameworks and libraries (Scikit-learn, Pytorch, Tensorflow, LightGBM, Keras, MLFlow etc.) and familiarity with LLM-specific frameworks (e.g., LangChain, Hugging Face Transformers, etc).
  • Hands on experience with one or more ML and GenAI/LLM cloud services (Amazon SageMaker, Amazon Bedrock, Databricks Mosaic AI, Seldon, Arize, etc)
• Experience contributing to various software architecture design, with some emphasis on scalable architectures supporting both traditional ML and advanced LLM workflows.
• Experience collaborating cross-functionally and contributing to technical decisions in team projects
• Experience with one or more relevant tools (Flink, Spark, Sqoop, Flume, Kafka, Amazon Kinesis, Terraform, Airflow)
• Ability to share findings in easy to consume formats, whether that is through dashboards or data modeling.
• Experience working in a cloud environment such as AWS, GCP, Azure.
• Experience with Databric