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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Staff Machine Learning Engineer - **Company:** FanDuel Inc - **Location:** New York, United States - **Salary:** $159,000.0 - $208,950.0 - **Contract:** Permanent contract - **Skills:** Flutter, Java (Programming Language), Artificial Intelligence, Airflow, Amazon Web Services, Automated Storage and Retrieval Systems, Microsoft Azure, Encodings, Computer Programming, Data Structures, Distributed Systems, Elasticsearch, Python (Programming Language), Tensorflow, Azure Machine Learning, Search Technologies, Software Engineering, Cloud Platform System, Pytorch, Large Language Models, Apache Spark, Scikit Learn, Apache Flink, Apache Kafka, Video Streaming, Software Coding, Terraform, GPT, Data Pipelines, Databricks - **Published:** September 4, 2026 - **Apply:** https://startup.jobs/staff-machine-learning-engineer-search-fanduel-7743164 ## About the Role * 7+ years of relevant experience developing code in one or more core programming languages (Python, Java, etc.) * 4+ years of experience designing and building scalable software architectures, including systems supporting ML, Search or LLM workloads * 2+ years of experience implementing vector search, semantic search, or embedding-based retrieval systems (RAG workflows) for production ML or AI applications with vector store such as AWS OpenSearch, Elasticsearch, etc * 2+ years of experience building platform components and frameworks improving ML/AI development and deployment efficiency * 1+ years of experience driving technical direction, making architectural trade-offs, and influencing engineering decisions across teams * 1+ years of experience collaborating cross-functionally, working with leadership and influencing and driving partner teams to adopt platform capabilities and best practices * 1+ years of experience conducting design reviews and setting engineering standards within a team * Experience with data and streaming technologies (e.g., Spark, Flink, Kafka, Airflow, Terraform) * Experience working in cloud environments such as AWS, GCP, or Azure * Experience designing and building data pipelines for production ML and GenAI/LLM systems * Strong understanding of data structures, distributed systems, and software engineering principles * Demonstrated ability to independently own and deliver complex technical projects end-to-end in ambiguous environments * Ability to communicate technical concepts and insights effectively through dashboards, data models, or design artifacts * Track record of mentoring engineers and elevating team-wide engineering practices and technical quality, * 1+ years of experience working with typeahead / autocomplete systems and integrating ML signals into query understanding or ranking workflows * 1+ years of experience combining outputs from multiple retrieval systems (e.g., vector search + typeahead + personalization models) to improve relevance * 1+ years of experience deploying ML and GenAI/LLM models under constraints of scalability, correctness, and maintainability in production environments + Hands-on experience with ML frameworks (Scikit-learn, PyTorch, TensorFlow, etc.) and familiarity with LLM frameworks + Hands-on experience with ML and GenAI/LLM platforms (e.g., SageMaker, Bedrock, Databricks, etc.) ## Description 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 systems incorporating typeahead search, vector search and ML personalization signals to optimize relevance and user experience, with end-to-end ownership from ideation to production * Building and scaling multi-layer serving architectures for ML and GenAI/LLM models, making key architectural decisions in ambiguous problem spaces * Driving the design and evolution of platform capabilities (e.g. CLI, SDK, Infra Automation, Platform Applications) to streamline ML and GenAI/LLM application development and deployment lifecycle across teams * Contributing to technical strategy and influencing adoption of ML and GenAI/LLM platform solutions across partner engineering teams * Applying best practices in data security, privacy (e.g. GDPR, CCPA), governance, and data testing frameworks to ensure reliable and compliant data products * Owning the continuous integration and delivery of production-grade data and ML systems with a focus on scalability, reliability, and cost-efficiency * An inclusive culture that expects excellence and prioritizes 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 * Operate as part of an autonomous team with end-to-end ownership of key components of our data and ML platform architecture * Set engineering standards and mentor junior engineers, elevating team practices in system design, reliability, automation, data quality, and operational excellence ## Related Videos - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [Flutter Packages and Plugins - A Look Under the Hood](https://www.wearedevelopers.com/videos/555-flutter-packages-and-plugins-a-look-under-the-hood) - [ Evaluating AI models for code comprehension](https://www.wearedevelopers.com/videos/1462-evaluating-ai-models-for-code-comprehension) - [Alibaba Big Data and Machine Learning Technology](https://www.wearedevelopers.com/videos/37-alibaba-big-data-and-machine-learning-technology) - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [AI Model Management Life Circles: ML Ops For Generative AI Models From Research to Deployment](https://www.wearedevelopers.com/videos/1152-ai-model-management-life-circles-ml-ops-for-generative-ai-models-from-research-to-deployment) ## Related Articles - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [Dev Digest 120 - Apple and peers](https://www.wearedevelopers.com/magazine/455-dev-digest-120-apple-and-peers) - [How Much FAANG Companies Actually Pay Software Engineers in 2025](https://www.wearedevelopers.com/magazine/230-how-much-faang-companies-actually-pay-software-engineers-in-2025)