Machine Learning Engineer Job Description (November 2026)

Need an expert Machine Learning Engineer fast? Use our job description to attract top engineers.

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Machine Learning Engineer job description template

Machine Learning is a subset of artificial intelligence that allows systems to learn from data and make decisions without explicit programming. By utilizing algorithms and statistical models, Machine Learning enables the creation of predictive models that can recognize patterns, forecast trends, and improve over time.

Machine Learning is applied across various industries, including finance, healthcare, e-commerce, and technology, for applications such as fraud detection, personalized recommendations, image and speech recognition, and predictive analytics. It powers advanced systems like recommendation engines, autonomous vehicles, and natural language processing tools.

Companies like Google, Amazon, and Tesla are at the forefront of using Machine Learning to enhance their services. From optimizing search results to improving voice assistants, Machine Learning is critical for companies seeking to deliver cutting-edge, data-driven solutions.

Machine Learning benefits from a strong ecosystem of frameworks and libraries like TensorFlow, PyTorch, and Scikit-learn, which streamline model development and deployment. With an active research community, abundant learning resources, and open-source tools, developers and data scientists can easily adopt and implement Machine Learning in real-world applications. The technology continues to evolve rapidly, ensuring it remains at the forefront of innovation.

If you’re looking for a Machine Learning Engineer, feel free to copy and customize the Machine Learning Engineer Job Description created by Lemon.io below. Or, skip the hassle and let us connect you with a top-tier, pre-vetted Senior Machine Learning Engineer in just 48 hours.

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Job Description:

We are looking for an experienced Machine Learning Engineer with [ X ] years of experience in a similar position. You will be responsible for designing and developing self-running AI systems and delivering robust solutions with our team of designers and developers.

Job Responsibilities:

Designing and developing systems using Machine Learning
Implementing new Machine Learning tools and algorithms
Examining data quality for Machine Learning (ML) applications
Establishing Machine Learning (ML) and data science prototypes
Conducting statistical analysis for Machine Learning (ML) models
Improving Machine Learning (ML) models using the results
Working in a team of other developers and designers, sharing ideas and developing prototypes
Running Machine Learning tests
Conducting code reviews and providing Machine Learning (ML) code quality and standards compliance
Being involved in Machine Learning (ML) processes of agile development

Required Skills:

[ X ] years of work experience in a similar position
[ X ] years of experience with Python and its frameworks, like PyTorch and Tensorflow, and SQL.
Familiarity with Hadoop, Spark, Pig, Hive or other big data frameworks **is a must
Proficiency with Theano, Tensorflow, Caffe
Good command of ML libraries and packages
Strong knowledge of AWS, GCP, Azure or other cloud platforms
Strong understanding of data structures and software architecture
Ability to visualize and manipulate large datasets is a must
Experience with Git or other version control systems
Excellent communication and collaboration abilities, with a proven track record of working effectively within a team environment
Strong analytical and problem-solving skills, dedicated to delivering high-quality solutions
Proficiency in English is required.

Perks and Benefits:

Competitive Salary
Health Insurance: medical, dental, and vision plans
Retirement Plans
Paid Time Off: vacation days, holidays, and sick leave
Flexible Work Arrangements: remote work options and flexible scheduling
Professional Development: learning and development opportunities, including training programs, workshops, and conferences
Wellness Programs: fitness classes, mindfulness sessions, and employee assistance programs
Commuter Benefits: public transit subsidies, parking discounts, and bike-to-work incentives
Employee Discounts: discounts on company products or services, perks and privileges from partner organizations
Life Insurance
Parental Leave: paid time off for maternity, paternity, and adoption leave
Career Growth Opportunities: promotions, lateral moves, and leadership development programs
Social Events and Perks: team outings, catered meals, snacks, and recreational activities

Salary Expectations:

$[ X ]k-$[ X ]k yearly

Hire remote Machine Learning engineers

Antonio
Back-end Web Developer
Spain/(GMT)
Antonio is a Python and Django backend engineer who also designs data pipelines and ML-heavy systems. Recently he architected a fuel-trading platform with dbt, Dagster and GCP, building load-optimization logic used by trading teams and a pipeline for 329 stations and 1,000+ tanks; earlier he scaled a scraping module 100x.
Experience
12 years
Availability
Full-time
Machine learningDjangoPythonPostgreSQLDockerAWSSeleniumPandasTDDGCPREST APIAPI TestingSelenium WebDriverAPINumPyGCP Compute EngineElasticSearchBeautifulSoupData ScienceNLPDocker ComposeReactDeep LearningRedisDjango RESTCeleryWeb scrapingAirflowDBTDagster
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Claudio
AI Engineer
Italy/(GMT +1:00)
Claudio is an AI engineer working in Python, AWS, LangChain, and TypeScript who has shipped LLM and ML systems as a hands-on CTO. He built a checkout donation optimizer that handled about 3B transactions a year, ran in under 30ms, and lifted donations by about 50%, then designed tenant-safe analytics and support chatbots for regulated data.
Experience
12 years
Availability
Full-time
Machine learningAWSPythonLLMAIOpenAIAWS Lambda.NETSolution architectureCloud ArchitectureJavaPHPKubernetesDockerLangChainRAGPySparkNode.jsTypescriptStripe APIC#MongoDBDynamoDBHerokuElasticSearch
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Filip
Full-stack Web Developer
Italy/(GMT +1:00)
Filip is a full-stack engineer working in Node.js, Next.js and TypeScript who has led public-sector platforms, including a climate governance system with AI-assisted copy and image generation for 40+ local entities. He also built a knowledge workspace as founding engineer, adding semantic search, end-to-end encryption, and a Chrome extension for one-click chat imports.
Experience
9 years
Availability
Full-time
Machine learningAWSReactTypescriptNext.jsNode.jsJavaSupabasePythonOpenAI APIGolangMongoDBPostgreSQLJavaScriptExpress.jsVercelOpenAITailwind CSSCI/CDWeb scrapingAWS SESAmazon S3GitHub ActionsStripe APIVue.jsChrome extensionsNest.jsDockerClaude CodeCursor
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Bradley
Full-stack Web Developer
United States/(GMT -8:00)
Bradley is a full-stack engineer working across React, Node.js, TypeScript and AWS who built a fitness app end to end, from media streaming and real-time sync to models that match music to workout intervals. Earlier he shipped features for Google Local Search and Maps used by millions daily.
Experience
13 years
Availability
Full-time
Machine learningNode.jsReactTypescriptAWSNext.jsRustRedisGCPKubernetesTensorflowCouchbaseBun.jsJavaScriptBigQueryGraphQLLLMOpenAIAIDockerTerraformSupabaseExpress.jsBullMicroservicestRPCReact NativeExpoCI/CDGitHub ActionsMonorepoTurborepo
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Amandeep
Data Scientist
Canada/(GMT -5:00)
Amandeep is a data scientist working in Python, scikit-learn, TensorFlow, Databricks, and MLflow, with recent finance work on real-time credit risk models deployed on Azure and AWS. In that system, she raised accuracy from 67.4% to 82% and delivered about 18% YoY impact through better risk decisions.
Experience
6 years
Availability
Part-time
Machine learningPythonLLMNumPyScikit-learnPandasKerasTensorflowMLflowLangChainOpenAISnowflakeMySQLData ModelingMicrosoft Power BIETLTableauSQL
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Brian
Machine Learning Engineer
Canada/(GMT -5:00)
Brian is an ML engineer who builds large-scale Python data and model platforms, most recently turning hundreds of millions of device signals into a cookieless household graph that lifted campaign ROI by 20 to 30%. He also built a cross-orchestrator MLOps layer for Kubeflow, Vertex AI, and Cloud Composer, and later shipped real-time audio classification and generative media pipelines on AWS.
Experience
8 years
Availability
Full-time
Machine learningData ScienceBig DataMLOpsPythonAIData analysisData WarehousePyTorchGCPAWSApache KafkaKubernetesAPIPySparkCI/CDDeep LearningApache SparkPandasScikit-learnNLPETLSciPyBigQueryNumPySQLLLMOpenAITensorflowLangChainPydanticVector Databases
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Adil
AI Engineer
United States/(GMT -8:00)
Adil is a Python AI engineer who builds production LLM and computer vision systems. He recently architected a legal document agent with source-cited answers for contracts and filings, and built a defense workflow that used parallel agents, confidence scoring, and human review to cut manual processing time by over 60%.
Experience
8 years
Availability
Full-time
Machine learningPythonAILLMAWSOpenAIKubernetesAPITensorflowLangGraphPyTorchGCPAI agent developmentMLOpsLangChainAmazon ECSClaude LLMPandasNLPpytestAzure DevOpsComputer VisionNumPyPrompt engineeringRAGDeep LearningOpenAI APIDockerOpenCVAWS LambdaAirflowTypescriptLinux
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Enes
Machine Learning Engineer
Turkey/(GMT +3:00)
Enes is an ML engineer working in Python, PyTorch and TensorFlow who builds NLP and computer vision systems end to end. He trained a vehicle classifier on 32+ million images and later led LLM work for ad generation, insurance document understanding, and a recommendation engine, including fine-tuning Mistral models and deploying APIs.
Experience
15 years
Availability
Part-time & Full-time
Machine learningPythonNLPAIPyTorchTensorflowAWSGCPETLMLOpsSQLDeep LearningMySQLScikit-learnOpenAI APIGPTLangChainAWS SageMakerLLMPandasMistral LLMHugging FaceSnowflakeClaude LLM
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Bruno
Full-stack Web Developer
Brazil/(GMT -3:00)
Bruno is a full-stack engineer working mainly in Node.js, TypeScript, Python, and .NET who leads products from scratch. Recently he built a farm operations platform where WhatsApp voice notes are transcribed into tasks, reaching 15 farms and 250 monthly users, and earlier cut ML video processing time by 68% in a cattle IVF system.
Experience
15 years
Availability
Full-time
Machine learningReactTypescriptJavaScriptNode.jsPythonAWSLLMASP.NET MVC.NETPostgreSQLOpenAPIClaude LLMNext.jsDockerDocker ComposeC#TensorflowCI/CDGoogle API and ServicesKubernetesReduxDjangoPyTorchNest.jsOpenAIAIAPIe2e testingGitHubTurborepoGCPshadcnAI agent developmentMicrosoft AzureFlaskOpenCVNumPyMySQL
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Machine Learning Engineer hiring resources

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Frequently asked questions

To become a Machine Learning Engineer, you need to master and continuously practice various aspects of the Machine Learning. If you would like to become a Machine Learning Engineer, you should start by learning Python and its frameworks, like PyTorch and Tensorflow, and SQL. You need to have strong knowledge of cloud platforms: AWS, GCP, Azure. Also, an understanding of data structures and software architecture is a must for a Machine Learning Engineer. You should study how to visualize and manipulate large datasets.

A Machine Learning Engineer is a tech specialist who uses Python and its frameworks to design and implement algorithms which can help machines make more clear decisions using data.

A Machine Learning Engineer creates, develops and improves AI systems that enable machines to make decisions and imitate the human learning using large datasets. They develop solutions for various businesses: fraud detection systems, healthcare applications, robots for navigation, image and video recognition systems.

You can assess the expertise of a Machine Learning Engineer by analyzing their level of core concept understanding, especially supervised/unsupervised learning, model training algorithms, and performance metrics, like accuracy, and precision. Give them some practical tasks like building or optimizing some models on real data. Find out what tools they have been working with. You must also analyze their problem-solving skills, knowledge of mathematics, including linear algebra and statistics, experience working with large datasets, data pre-processing, and feature engineering, and how they can handle bulky volumes of information. Finally, review their knowledge of cloud platforms for model deployment using AWS and Azure, version control usage, and continuous integration and deployment pipelines.

A Machine Learning Engineer needs strong skills in Python, data structures, and mathematics (statistics, linear algebra, probability). They should understand machine learning algorithms, model training, and data preprocessing. Experience with frameworks like TensorFlow, PyTorch, and scikit-learn, along with knowledge of SQL, APIs, and cloud platforms (AWS, GCP, Azure), is also essential.

Yes, Machine Learning Engineers are still in high demand. As AI continues to expand across industries like healthcare, finance, and e-commerce, companies need experts to build and deploy intelligent systems. Demand is especially strong for engineers skilled in Python, data modeling, and frameworks like TensorFlow or PyTorch.

As a Machine Learning Engineer, your salary depends on your experience, technical expertise, and years of working with AI and data-driven technologies. The employment type also impacts pay — in-house engineers and independent contractors often earn different hourly rates based on project scope, location, and seniority level. See the full breakdown by seniority level and tech stack in our 2026 Software Developer Salary Report.

A Machine Learning Engineer is responsible for designing, developing, and deploying AI models that enable systems to learn and improve automatically. The role includes data preprocessing, model training, and performance optimization using frameworks like TensorFlow or PyTorch. Machine Learning Engineers collaborate with Data Scientists and Software Developers to build scalable, intelligent solutions.

Both AI Engineers and Machine Learning Engineers earn competitive salaries, but AI Engineers typically make slightly more due to their broader focus on developing and integrating complex AI systems. However, the difference depends on experience, specialization, and project type, as skilled Machine Learning Engineers in high-demand industries can earn similar compensation.

You can find Machine Learning Engineers on platforms like LinkedIn and Indeed, where many professionals highlight their AI and data science experience. For a faster and more reliable option, use Lemon.io — a vetted talent marketplace that connects startups with pre-screened Machine Learning Engineers skilled in Python, TensorFlow, and AI model development.

Machine Learning Engineers use technologies like Python, TensorFlow, PyTorch, and scikit-learn for model development and training. They rely on data tools such as Pandas, NumPy, and SQL for data processing, and cloud platforms like AWS, Google Cloud, or Azure for deployment. They also use Docker, Kubernetes, and MLflow for model tracking, automation, and scalability.