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Staff Machine Learning Engineer-ML/AI Platforms
| Company | FanDuel |
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| Location | Atlanta, GA, USA |
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| Salary | $158000 – $208000 |
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| Type | Full-Time |
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| Degrees | |
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| Experience Level | Senior |
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Requirements
- 5-7+ years of relevant experience developing code in one or more core programming languages (Python, Java, etc.)
- 7+ Years of 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)
- 5+ Years of experience designing and building various software architecture with some emphasis on scalable architectures supporting both traditional ML and advanced LLM workflows.
- Deep understanding and knowledge of data structures, distributed computing, and software engineering principles
- 4-5+ Years of experience demonstrating technical leadership working with teams, owning projects, defining, and setting technical direction for 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.
- Conduct regular design process reviews and ensure development standards within the team.
- Working with leadership to drive adoption of ML and GenAI/LLM solutions to product engineering teams.
- Experience working in a cloud environment such as AWS, GCP, Azure.
- Experience with Databricks is a plus, their unity catalog, another plus.
- Designing and building data pipelines for production level ML and GenAI/LLM infrastructure.
- Experience with vector databases to efficiently manage and retrieve embeddings for LLM applications, enabling high-performance similarity search and retrieval-augmented generation (RAG) workflows is a plus.
- Motivate junior engineers on best practices and latest industry design patterns.
Responsibilities
- Building and scaling multi-layer serving architectures for ML and GenAI/LLM models
- Developing platform features and capabilities (e.g. CLI, SDK, Infra Automation, Platform Applications) for streamlining model 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
- Apply your experience and intellect as part of an autonomous team with end-to-end ownership of key components of our data architecture.
- Serve as a mentor to more junior engineers not only in cultivating craftsmanship but also in achieving operational excellence – system reliability, automation, data quality, and cost-efficiency.
Preferred Qualifications
- Experience with Databricks is a plus, their unity catalog, another plus.
- Experience with vector databases to efficiently manage and retrieve embeddings for LLM applications, enabling high-performance similarity search and retrieval-augmented generation (RAG) workflows is a plus.
