Role Description Edelman is seeking a talented and experienced Senior Machine Learning Engineer to join our remote-first global product development team. In this role, you will help design, build, evaluate, and deploy machine learning and generative AI solutions that power actionable PR and communications insights for our clients. This is a hands-on role for someone who enjoys working across the full ML lifecycle, from problem framing and experimentation through production deployment and iteration. You will partner closely with product, engineering, design, and data teams to deliver scalable, high-impact AI products. Key Responsibilities - Design, build, and deploy machine learning and GenAI solutions for production use. - Develop models and systems across a range of problem types, including structured data, unstructured text, time series, and LLM-powered workflows. - Partner with product managers, designers, data engineers, MLOps engineers, and frontend/backend developers to deliver end-to-end product solutions. - Work cross-functionally to define ambiguous problems, conduct user discovery, and translate insights into effective product and technical solutions. - Evaluate model and system performance using appropriate metrics, and iterate based on results. - Contribute to architecture and implementation decisions that balance performance, reliability, scalability, and cost. - Document technical approaches, experiments, and implementation details to support maintainability and future development. - Communicate technical findings, tradeoffs, and recommendations clearly to both technical and non-technical stakeholders. - Help establish best practices for experimentation, model deployment, and AI product development. Qualifications - 5+ years of experience in machine learning, applied data science, or a related field. - Proven experience building and deploying production-ready machine learning systems. - Strong experience with Python; familiarity with SQL and data querying workflows. - Experience working with structured and unstructured data, including classical ML approaches and modern LLM-based systems. - Experience across the ML lifecycle, including problem definition, exploratory analysis, feature or prompt development, model evaluation, deployment, and monitoring. - Familiarity with version control and ML/DevOps tooling such as Git and MLflow. - Experience working with cloud-based data and ML platforms such as Databricks, AWS, Azure, or GCP. - Strong communication skills, including the ability to collaborate with users and non-technical stakeholders throughout discovery and delivery. Preferred Qualifications - Experience with OpenAI APIs or similar LLM platforms, including prompting, evaluation, and application development. - Experience using coding agents, spec-driven workflows, or AI-assisted development practices in cross-functional product or engineering environments. - Experience building GenAI features for user-facing products. - Comfortable working through ambiguity and contributing to solutions from problem framing through delivery. - Experience in Agile or Scrum environments. - Experience mentoring other practitioners or providing technical leadership on projects. - Experience owning features end-to-end, from early problem framing and discovery through development, launch, and iteration. What Success Looks Like - You ship reliable ML and GenAI capabilities that create measurable value for clients and internal users. - You make sound technical decisions and communicate them clearly. - You help improve how the team experiments, deploys, and scales AI solutions over time.