AI/ML Engineer

Nexstar Media Group, Inc.•New York, NY

46d•$90,000 - $110,000•Onsite

About The Position

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.

Requirements

Bachelor's degree required. Math, Statistics, Computer Science, or Information Science preferred
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)
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)
Must be eligible to work in the United States.

Nice To Haves

Domain experience in media, advertising technology, or marketing analytics
Experience deploying AI/ML systems at scale, including CI/CD for ML (MLOps)
Databricks / Apache Spark for large-scale data processing and ML pipelines
Kubernetes experience for orchestrating containerized workloads
Experiment tracking tools such as MLflow or Weights & Biases
Recommender systems development, particularly using GCP recommendations or NVIDIA Merlin
Neural network development using PyTorch, TensorFlow, or JAX

Responsibilities

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