Nexstar Media · New York, NYPrepare for this interview
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Nexstar is looking for an ML / AI Engineer to join our growing data science team in New York City. In this role you will design, build, and deploy machine learning and agentic AI systems that power real-world products. You will work across the full ML lifecycle, including data preparation, model training, evaluation, deployment, monitoring, and iteration. You will have the opportunity to work on both classical ML applications and agentic systems, including multi-step reasoning pipelines and connected agent architectures. We value engineers who think rigorously about problem framing and can further business objectives with ML/AI solutions. Please note: Cover letter required when applying. Responsibilities include: End-to-end model development and deployment for production use cases Building, tuning, and evaluating agentic AI frameworks for connected, multi-step applications Ownership of the full model lifecycle, including training, evaluation, containerization, deployment and monitoring Maintaining cloud-based ML infrastructure on GCP or AWS Writing clean, well-tested Python code and contributing to shared libraries and internal tooling Collaborating with product, data engineering, and analytics teams to translate business problems into ML solutions Staying up to date with current ML/AI research and advocating for the adoption of new techniques where appropriate About You: Education: Bachelor's degree required. Math, Statistics, Computer Science, or Information Science preferred Engineering & ML: 2 - 5 years of hands-on ML engineering experience in a professional setting Strong Python proficiency with production-quality coding standards Solid ML fundamentals using Pandas, Scikit-learn, and XGBoost (or similar GBDT frameworks) Demonstrated experience deploying models to production Familiarity with Generative AI concepts: LLMs, prompt engineering, RAG, and the distinction between LLM APIs and agentic frameworks (e.g., LangChain, LangGraph) Infrastructure & DevOps: Docker experience, comfortable containerizing ML workloads for reproducible deployments Cloud infrastructure experience on GCP or AWS (e.g., GCS/S3, Vertex AI, SageMaker, Cloud Run, EC2)
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