AI/ML Engineer

About the role

Nexstar is looking for an ML / AI Engineer to join our growing data science team in New York City. This role involves designing, building, and deploying 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.

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

Requirements

• 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)

Preferred Qualifications

• 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

Benefits

• Medical, life and short-term disability insurance
• Worker’s compensation
• Unemployment insurance
• 401(k) retirement plan
• Paid holidays
• Paid vacation
• Paid sick time
• Employee assistance program
• Bereavement leave
• Jury duty service time off
• Military leave
• Medical/maternity & family leave

Pay

The salary range for this position is $90-$110K.