Machine Learning Engineer - Remote US

# Machine Learning Engineer ## About the Role McKinsey & Company is seeking a Machine Learning Engineer to join its People Analytics and Measurement team, working at the intersection of machine learning, natural language processing, MLOps, and cloud computing. This role offers ownership across the full machine learning lifecycle, with a strong emphasis on software engineering and production deployment. You will work with data engineers, developers, and product managers to build scalable, reliable, and impactful machine learning services and applications that support people analytics, recruiting, staffing, training, team composition, employee satisfaction, and other high-impact use cases. ## Key Responsibilities * Leverage internal and external datasets to research and develop algorithms supporting people analytics use cases.* Develop machine learning solutions for recruitment, training, staffing, team composition, employee satisfaction, and related applications.* Design, implement, deploy, and integrate state-of-the-art machine learning services into critical workflows at scale.* Deploy machine learning services to cloud environments and maintain production performance and reliability.* Build and maintain stable, performant data science services for both real-time and scheduled workloads.* Develop NLP tools and services that transform unstructured text into actionable insights.* Analyze resumes, job descriptions, surveys, and other organizational documents using natural language processing techniques.* Build self-service analytics tools for data scientists and other stakeholders.* Develop and expand NLP, MLOps, and machine learning capabilities across the organization.* Experiment with emerging technologies and share knowledge and capabilities with colleagues.* Collaborate with data engineers, developers, and product managers to deliver scalable machine learning applications.* Communicate analytical processes, methodologies, and insights to both technical and non-technical audiences.* Apply software engineering and DevOps best practices throughout the machine learning lifecycle. ## Required Qualifications * Bachelor’s degree in a quantitative discipline such as statistics, mathematics, econometrics, computer science, economics, physics, or a related field.* Strong theoretical and applied understanding of natural language processing, machine learning, statistical methods, optimization, and graph algorithms.* Experience with language transformers, large language models, text embeddings, and topic modeling.* Knowledge of statistical techniques such as linear and logistic regression.* Knowledge of machine learning methods including boosted trees and deep neural networks.* At least 2 years of experience or exposure to establishing, optimizing, troubleshooting, and maintaining AWS cloud architecture for performance and stability.* Experience specifying infrastructure as code using AWS CloudFormation and/or Terraform.* Experience maintaining CI/CD automation, primarily using GitHub Actions.* Experience containerizing code for service-based architectures using Docker.* Strong Python programming skills.* Strong analytical and problem-solving abilities.* Understanding of software engineering and DevOps best practices, including version control and testing.* Ability to communicate effectively in a remote working environment.* Distinct customer focus and a strong quality mindset. ## Preferred Qualifications * Master’s degree or PhD in a quantitative discipline.* At least 3 years of career experience developing production-oriented systems in Python.* Knowledge of R.* Experience with SQL.* Strong understanding of database design principles.* Experience with data warehousing and cloud solutions such as Snowflake.* Experience with graph databases such as Neo4j. ## Skills & Competencies * Machine Learning* Natural Language Processing (NLP)* Large Language Models (LLMs)* Language Transformers* Text Embeddings* Topic Modeling* Statistical Modeli