Job Description

Position Summary:

Nexstar is looking for an AI Architect 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/AI 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.

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, AutoGen)

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

NOTE: This job description contains the basic requirements for the position and is NOT intended to be a complete list of responsibilities; other duties may be assigned.

Compensation: $100K/yr - $120K/yr, commensurate with experience.

Benefits: Our comprehensive benefits package for full-time employees includes, but not limited to, medical (with multiple plan options), dental, and vision insurance, paid time off, a 401(k) with company matching contributions, Paid Parental leave, life insurance and more. Part-time employees are eligible for 401(k) and any local/state leave plans. Union positions are subject to the benefits outlined in their Collective Bargaining Agreement.

EEO Statement: All qualified applicants will receive consideration for employment without regard to disability, protected veteran status, or any other characteristic protected by local, state, or federal laws, rules, or regulations.