WHAT DOES A MACHINE LEARNING ENGINEER DO?
Reviewed by Lam Nguyen under Editorial Standards
Published: Mar 10, 2026. The Machine Learning Engineer leads the end-to-end development of scalable AI and ML solutions from data processing to production deployment. This role partners with cross-functional teams to translate business needs into impactful and reliable machine learning systems. The engineer also drives innovation and ownership and ensures quality, scalability, and continuous improvement.
A Review of Professional Skills and Functions for Machine Learning Engineer
1. Machine Learning Engineer Duties
- Machine Learning: Work with other data scientists on the team to develop both short-term and long-term strategies around machine learning for forecasting
- Cross-Functional Collaboration: Collaborate with the hardware and software teams to tune and integrate the solutions developed
- Monitoring Reporting: Develop and maintain internal monitoring and reporting tools to enable being proactive and demonstrating value to customers
- Platform Development: Build up the Machine Learning and Experimentation platform for data scientists to work with
- Data Engineering: Implement data pipelines that allow for versioning and implementation of the training datasets
- Team Mentorship: Collaborate with, mentor, and influence members within the team to drive the development process to enable data scientists to deliver their best work
- Code Quality: Write clean, well-tested, and maintainable code and peer review team members’ code
- Algorithm Development: Develop novel algorithms and functional prototypes using NLP and machine learning techniques
- Information Extraction: Create product-informed solutions related to information extraction, word association, and topic modeling using structured and unstructured clinical and user-generated data
- NLP Strategy: Provide NLP guidance for the company, including defining best practices, product roadmap and strategy
- Production Deployment: Deliver end-to-end, scalable, performant, and reliable NLP and machine learning features in production
2. Machine Learning Engineer Details
- Data Profiling: Profile the data structure and data format of the platform to identify opportunities for improvement so that it can be more relevant for future ML and AI applications
- ML Problem Framing: Collaborate with Data Scientists to break down potential predictive and prescriptive product ideas into ML problems and research potential approaches
- Data Preparation: Help Data Scientists perform statistical analysis, data collection and data processing to prepare clean and relevant datasets that are ready for the training process
- Feature Engineering: Collaborate with a Data Scientist in feature engineering and the model training process
- Model Deployment: Support Data Scientists in deploying data models and data pipelines for feature engineering
- Prototype Development: Build a proof of concept or prototype around the ML model to demonstrate the potential of product features
- Model Integration: Work with a Data Engineer and a Software Engineer on the integration of an ML model into the platform and products
- Model Monitoring: Work with a Data Engineer to build monitoring services for ML models and optimize the deployment process
- Model Versioning: Work with a Data Engineer to implement the model versioning framework and automatic retraining
3. Machine Learning Engineer Responsibilities
- Stakeholder Communication: Talk to individual stakeholders to understand their pain points and interactions with the DS team
- Infrastructure Planning: Gain an understanding of the infrastructure and roadmap
- End-to-End Learning: Work with an ML engineer on a project to gain an end to end understanding of the approach to model development
- Project Ownership: Take ownership of a machine learning project
- ML Workflow Design: Create the end-to-end machine learning workflow for the project, including the ingestion pipeline, feature engineering, model development, training, testing, serving, and monitoring
- Technical Documentation: Document the entire approach
- Objective Alignment: Collaborate with managers to determine and refine machine learning objectives
- Workflow Optimization: Suggest improvements to the ML workflow
- Framework Development: Work on developing a framework for faster ML development
- Experimentation Platform: Create an AB testing platform
- Feature Store: Design a feature store to include online and offline features
- Advanced Modeling: Use cutting-edge models in NLP and RL to develop user recommendations
- Center Excellence: Help develop DS as a Center of Excellence
4. Machine Learning Engineer Accountabilities
- Code Review: Participate in the code review process
- ML Pipeline Development: Design and build efficient and reproducible ML pipelines producing features and components to iterate over a model including feature selection, hyperparameter tuning, and validation
- Production Operations: Schedule and operate a model in a production data pipeline
- End-to-End Deployment: Design and build an ML pipeline end-to-end to promote ML artefacts into production
- Performance Analysis: Analyze and communicate model performance
- Clean Coding: Write clean and tested code that can be maintained and extended by other software engineers
- Technology Research: Keep up to date with relevant technologies and frameworks and propose new ones that the team can leverage
- Cross Team Framework: Build a framework to coordinate between teams including DevOps, Data Engineers, QA, MLE, and DS
5. Machine Learning Engineer Functions
- Product Optimization: Use and build ML pipelines and algorithms to improve the product
- Speech NLP Systems: Work on Speech to Text, Natural Language Processing, Dialogue Management, and Text to Speech
- Research Implementation: Track and analyze the current state of the literature and implement relevant approaches
- Domain Understanding: Learn about the healthcare sector and customer needs to better design solutions
- Executive Reporting: Report directly to the Chief Technology Officer
- Quality Balance: Balance speed and quality with a focus on tangible results
- Model Implementation: Work on implementing new machine learning models in collaboration with Data Scientists
- Feature Automation: Support product development by creating new features and automating processes
- Scalable Systems: Build and maintain scalable solutions in a production environment
- Client Integration: Onboard new clients and support the integration of the solution on the client side
- Technical Documentation: Document new services and features
- Issue Investigation: Investigate complex production issues by recreating problems and utilizing trace files and error diagnostics
- Root Cause Analysis: Identify root causes and propose solutions
6. Machine Learning Engineer Overview
- ML Domain Expertise: Participate in and share own perspective within the domain of machine learning, provide subject matter expertise in design or project reviews and project meetings
- Project Ownership: Take responsibility for small projects or own part of a larger project and complete tasks promptly according to project requirements
- Team Support: Seek assistance to solve problems and help other team members
- Cross-Functional Collaboration: Collaborate with cross-functional peers on tasks
- Data Engineering: Complete complex tasks and solve issues related to the engineering and management of machine learning data with minimal guidance from more experienced engineers
- Algorithm Prototyping: Develop, adapt, or prototype complex machine learning algorithms, models, or frameworks aligned with and motivated by product proposals or roadmaps with minimal guidance from more experienced engineers
- Experimentation: Conduct complex experiments to train and evaluate machine learning models or software independently
- Model Optimization: Create and execute complex methods to optimize new or existing machine learning algorithms, models, kernels, and execution frameworks
- Problem Solving: Suggest possible solutions to issues and document lessons learned
- Production Integration: Assist with the integration of machine learning algorithms into a platform or product for production
- Implementation Support: Help resolve issues during implementation
- Industry Research: Seek essential knowledge of machine learning industry trends, competitors' products, and advances within the area of expertise from publicly available information and research
7. Machine Learning Engineer Details and Accountabilities
- Machine Learning Research: Participate in cutting-edge research in machine learning and its applications to drug discovery, design, and development
- Cross Functional Collaboration: Collaborate closely with cross-functional teams across Prescient Design and gRED to solve complex problems in the life sciences
- Data Pipeline Management: Help manage and scale data pipelines for training and inference
- ML Framework Engineering: Solve core engineering challenges including the design, implementation, and scaling of machine learning frameworks and algorithms
- Production Engineering: Design, develop, and implement production-level code that serves billions of search requests
- Ranking Optimization: Design and apply data-driven and machine learning techniques to provide optimal ranking
- Full Cycle Development: Own the full development cycle including design, development, impact assessment, AB testing including interpretation of results, and production deployment
- Feature Development: Develop new ranking features and techniques, building upon the latest results from the research community
- Technical Problem Solving: Collaborate with other engineers within Amazon to find technical solutions to complex design problems
- Agile Delivery: Work in an agile environment to deliver high-quality software against aggressive schedules
- Research Development: Participate in aspects of the research and development process, from experimenting with new ideas to exploring new techniques
8. Machine Learning Engineer Tasks
- ADAS Benchmarking: Develop artificial intelligence technologies to create an automated benchmark system for ADAS perception
- Dataset Development: Drive the continuous growth of the JLR dataset
- Sensor Fusion: Develop tightly and loosely coupled fusion algorithms
- KPI Definition: Establish JLR KPI for autonomous driving solutions
- TRL Evaluation: Support technology readiness level evaluation
- Technical Assessment: Support the autonomous technologies technical assessment to enable certification and compliance activities
- Algorithm Deployment: Deploy AI algorithms to a wider software stack
- Internal Deployment: Deploy AI algorithms to internal customers
- AI Research: Research and implement the latest cutting-edge AI technologies
9. Machine Learning Engineer Roles
- ML Application Development: Develop machine learning applications to improve client experiences
- Data Modeling: Design and create appropriate datasets and data schemas for machine learning and data analysis
- Algorithm Research: Research and implement appropriate machine learning algorithms and tools
- Experimentation Analysis: Conduct machine learning experiments to validate model improvements and measure impact on end users
- Statistical Validation: Perform statistical analysis to validate, tune, and improve models
- Model Retraining: Implement systems to retrain models
- Business Analysis: Identify and define business cases where the model should be used with a strong business understanding
- Data Exploration: Perform data mining, feature selection, exploratory analysis, and search for new data sources
- Model Benchmarking: Research and benchmark supervised and unsupervised machine learning models according to the business case
- Model Deployment: Prepare datasets, train, tune, test, and deploy models to both test and production environments
- Model Maintenance: Maintain existing models, perform quality assurance, and monitor and evaluate models
10. Machine Learning Engineer Additional Details
- Communication Skills: Organize and express ideas and information clearly using appropriate and efficient methods of conveying information
- Self Awareness: Identify personal strengths and weaknesses and target areas for self-development
- Continuous Learning: Participate in educational opportunities
- Skill Development: Work towards mastering tasks by developing new skills or enhancing existing skills
- Initiative Taking: Look for opportunities to take on more responsibility
- Proactive Problem Solving: Take a proactive approach to anticipate and prevent problems
- Analytical Thinking: Break down problems and issues into components and analyze the costs, benefits, opportunities, and risks associated with each alternative solution
- Independent Collaboration: Work independently and in team settings to solve problems
- Adaptability: Handle changes and respond to setbacks with minimal disruption
- Technology Integration: Evaluate and integrate new technologies, processes, or methods into the workplace
- Time Management: Handle multiple assignments and priorities while fulfilling all commitments
- Flexibility: Accept new responsibilities and adapt to changes in procedures
- Quality Assurance: Ensure that quality standards are met and all procedures are followed
- Continuous Improvement: Take steps to correct mistakes and improve the overall product
Job Role FAQs
What is a job role?
A job role refers to the duties, responsibilities, and expectations associated with a specific position within an organization. It explains what tasks an employee performs, how they contribute to team objectives, and how their work supports the company’s overall goals.
What are the typical responsibilities of a job role?
Typical job role responsibilities include completing daily tasks, collaborating with team members, making decisions, and meeting performance targets. For example, a software developer may write code, fix bugs, review pull requests, and collaborate with product teams.
What is the difference between a job role and a job title?
A job title is the official name of a position, such as Marketing Manager or Software Engineer. A job role describes the actual duties, responsibilities, and expectations associated with that position.
Why are clearly defined job roles important?
Clearly defined job roles help organizations improve productivity, reduce workplace confusion, and ensure accountability. When employees understand their responsibilities and expectations, teams can collaborate more effectively.
How do job roles support career development?
Understanding different job roles helps professionals identify career paths and the skills required for advancement. By learning the expectations of various roles, individuals can build relevant skills and plan long-term career growth.
Editorial Process
Lamwork content is developed through structured review of publicly available job postings and documented hiring trends.
Editorial operations are managed by Thanh Huyen, Managing Editor, with research direction and final oversight by Lam Nguyen, Founder & Editorial Lead. Content is periodically reviewed to reflect observable labor market changes.