Senior Machine Learning Engineer
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Job Description
Senior Machine Learning Engineer designing and deploying machine learning solutions for Edelman. Collaborating with cross-functional teams to deliver impactful AI products in a remote-first environment.
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
Requirements:
- 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
Benefits:
- Diversity, equity, inclusion and belonging initiatives
- Flexible working arrangements
- Professional development opportunities
- Collaboration in a supportive environment
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