WHAT DOES A MACHINE LEARNING ENGINEER DO?

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

Editorial Process and Content Quality

This content is developed by the Lamwork Editorial Team using structured analysis of real-world job data, skill requirements, and hiring patterns.

Research framework by Lam Nguyen, Founder & Editorial Lead.

Reviewed by Thanh Huyen, Managing Editor.

Learn more about our editorial standards.

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