Machine Learning Engineer job description template

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Tips for Writing a Machine Learning Engineer Job Description

Machine Learning Engineers build artificial intelligence (AI) systems using large amounts of data. Predictive modeling, chatbots, and translation services heavily use AI systems. The job description template below will help you create a compelling job advertisement to attract the best candidates with the technical expertise and passion for innovation needed for success. Connect with your ideal Machine Learning Engineer through a job description that draws the right pool of applicants.

The Job Overview 

As a progressive company deeply invested in artificial intelligence, we hold innovation as a guiding principle. In line with our commitment to advancing this domain, we're searching for a seasoned Machine Learning Engineer to refine our existing systems and architect new AI models. This role will involve a rich blend of tasks. One of your core duties will be to collaborate intensively with data scientists and domain specialists, helping to develop AI models capable of tackling intricate business conundrums. 


  • Collaborate with cross-functional teams to develop and implement machine learning models and algorithms
  • Analyze large, complex datasets to extract actionable insights and improve machine learning model performance
  • Develop and maintain machine learning pipelines, including data preprocessing, feature extraction, model training, and evaluation
  • Deploy machine learning models into production environments and monitor their performance
  • Stay current with industry advancements in machine learning and artificial intelligence, and apply relevant techniques and technologies to improve existing models and systems
  • Contribute to the development of best practices, guidelines, and standards for machine learning engineering within the organization
  • {{Add any other responsibilities specific to your organization or project}}

Skills and Qualifications

  • Tertiary degree in Computer Science, Software Engineering, or a related field, with a focus on machine learning or artificial intelligence
  • 5+ years experience as a Machine Learning Engineer or a similar role
  • Advanced skills  in programming languages such as Python, Java, or C++
  • Experience with machine learning frameworks such as PyTorch and libraries (e.g., NumPy, Pandas, or Keras)
  • Adept at using big data processing tools (e.g., Hadoop, Spark) and data storage systems (e.g., SQL, NoSQL)
  • Excellent problem-solving and analytical skills
  • Strong communication and collaboration abilities
  • Ability to work independently when needed
  • {{List any additional qualifications or certifications specific to your organization or project}}


Rating is 4.8 out of 5.

clients rate Machine Learning Engineers based on 7K+ reviews

Hire Machine Learning Engineers

Machine Learning Engineers you can meet on Upwork

  • $90 hourly
    Austin F.
    Machine Learning Engineer
    • 5.0
    • (7 jobs)
    Brandon, MS
    vsuc_fltilesrefresh_TrophyIcon Machine Learning
    Amazon Web Services
    QA Automation
    Data Visualization
    Unit Testing
    Data Analytics
    ML Automation
    Data Science
    I am a software developer and data professional with over five years experience. My business philosophy is to provide solutions that generate value for the client long after I deliver them. I'm currently undergoing rigorous study to better understand and integrate various technologies to offer more comprehensive support to my clients. I can help implement: - various types of automation, including quality assurance automation - certain cloud solutions with GCP, AWS, and Microsoft AzureML - data transformations - machine learning models - dashboards - command-line interfaces - financial analyses - Jupyter notebooks - spreadsheet solutions (Google Sheets and Excel) - various types of interactive visualizations - software modules (in particular, I'm currently learning to build Python modules in Rust for faster performance) I have formal training as an engineer up to the Master's level. I have training from past full-time roles as research engineer and data analyst. I attribute much of my current skills to ongoing self-study using online resources such as Packt and O'Reilly technology and business training. I am also developing my skills in Rust and online cloud services. As a research engineer, I developed experimental machine learning models with Python and wrote corresponding technical reports. These efforts were also the subject of my graduate work. As a data analyst, I collected and analyzed data from solar energy infrastructure projects and conducted external market research to determine future project viability in different regions. Since joining Upwork, I have assisted clients with ML and data engineering tasks. As mentioned earlier, I am currently training to be a full-stack solutions architect with both coding and strategic planning offerings.
  • $35 hourly
    Karthick N.
    Machine Learning Engineer
    • 4.9
    • (18 jobs)
    Namakkal, TN
    vsuc_fltilesrefresh_TrophyIcon Machine Learning
    Website Content
    Internet of Things Solutions Design
    Ruby on Rails
    Artificial Intelligence
    Computer Vision
    Deep Learning
    I've studied computer science. I have an experience of Web Development with the flavor of HTML, CSS, Bootstrap, JavaScript and other web development tools. I really enjoy the fact that thousands of users use applications that are developed by me. The ultimate dream is that one day thousands will grow into millions or billions. I HAVE A DREAM! Overall if summarized my experience that would be exploring, organizing information, problem-solving, and implementation. Languages are essential for expressing your programming skills overall. From the EXPLORING attribute, I have worked around lots of different languages. 1) Ruby 2) AngularJS 3) Javascript 4) Vuejs 5) Python ( a new sensation I always wanted to explore Erlang but then I found this beauty. Python leverages the Erlang VM, known for running low-latency, distributed and fault-tolerant systems, while also being successfully used in web development and the embedded software domain.) In assistance to above languages below frameworks come into play, 1) Ruby on Rails 2) Django Databases are the main central storage of any web application. I got experience in both SQL and NoSQL 1) Postgres 2) MongoDB 3) SQLite 4) Mysql The game never ended on the server-side for me. The frontend/public-facing part of the web application has been also highly evolved. Everyone wants to use Single Page Applications - The SPAs. I got experience in the following 1) Angular JS 2) React JS Testing and Test Driven Development(TDD) is also an essential thing for any solid application. I can write automated tests in following 1) Rspec 2) Capybara Deployment is essential to distribute your application out in the wild. I got experience in the following tools and technologies 1) AWS 2) Google Cloud Platforms 3) Capistrano 4) Mina 5) Nginx 6) Passenger Phusion 7) Puma 7) Unicorn
  • $50 hourly
    Redis M.
    Machine Learning Engineer
    • 4.9
    • (14 jobs)
    Tirana, DU
    vsuc_fltilesrefresh_TrophyIcon Machine Learning
    Digital Marketing
    Data Analysis
    Cloud Computing
    Cybersecurity Tool
    Infrastructure as Code (IaC),Terraform,CloudFormation,Ansible,Puppet,Chef,Continuous Integration (CI),Jenkins,GitLab CI,Travis CI,CircleCI,Continuous Deployment (CD),Blue-Green Deployment,Canary Deployment,A/B Testing,Docker,Kubernetes,ECS (Elastic Container Service),EKS (Elastic Kubernetes Service),AKS (Azure Kubernetes Service),Fargate,Serverless Computing,AWS Lambda,Azure Functions,API Gateway,AWS CodePipeline,Azure DevOps,GitHub Actions,Infrastructure Monitoring,CloudWatch,Azure Monitor,Prometheus,Grafana,ELK Stack,Log Analytics,Application Performance Monitoring (APM),New Relic,Datadog,Dynatrace,Security Automation,AWS Security Hub,Azure Security Center,IAM (Identity and Access Management),AWS Organizations,Azure Active Directory,Multi-Factor Authentication (MFA),Network Security Groups (NSG),Azure Firewall,WAF (Web Application Firewall),Secrets Management,AWS Secrets Manager,Azure Key Vault,HashiCorp Vault,Compliance as Code,AWS Config,Azure Policy,Security Scanning,AWS Inspector,Azure Security Center Vulnerability Assessment,DevSecOps,Threat Modeling,Chaos Engineering,Fault Injection Testing,Circuit Breaker Pattern,Canary Analysis,Trunk-Based Development,GitFlow,Feature Toggles,Canary Releases,Rollback Strategies,Blueprints,AWS Well-Architected Framework,Azure Architecture Center,Cloud Design Patterns,Infrastructure Cost Optimization,AWS Cost Explorer,Azure Cost Management,Spot Instances,Reserved Instances,Autoscaling,Horizontal Scaling,Vertical Scaling,Cloud Migration Strategies,Lift and Shift,Replatforming,Refactoring,Retiring,Immutable Infrastructure,Immutable Deployment,Cloud Native Applications,Monolith Decomposition,Legacy System Integration,Hybrid Cloud Deployment,Cloud Storage Options,Amazon S3,Azure Blob Storage,EFS (Elastic File System),Azure Files,RDS (Relational Database Service),Azure SQL Database,DynamoDB,Cosmos DB,Redshift,Azure Database for PostgreSQL,Database Replication,High Availability,Disaster Recovery,Backup and Restore,Cross-Region Replication,Data Encryption,AWS KMS,Azure Storage Service Encryption,VPN (Virtual Private Network),Direct Connect,ExpressRoute,Transit Gateway,Azure Virtual Network,VPC (Virtual Private Cloud),Network Peering,Site-to-Site VPN,Cloud-Native Security,AWS WAF,Azure DDoS Protection,Web Application Firewall (WAF),Secure Gateway,Secure Enclaves,Threat Intelligence,Penetration Testing,Incident Response,AWS CloudTrail,Azure Monitor Logs,SIEM (Security Information and Event Management),Intrusion Detection System (IDS),Intrusion Prevention System (IPS),Identity Federation,Single Sign-On (SSO),OAuth,OpenID Connect,SAML (Security Assertion Markup Language),RBAC (Role-Based Access Control),ABAC (Attribute-Based Access Control),Zero Trust Security Model,Least Privilege Principle,IAM Policies,Azure RBAC,AWS IAM Roles,Network ACLs (Access Control Lists),Security Groups,Data Loss Prevention (DLP),Encryption Key Rotation,Secure Coding Practices,Static Code Analysis,Dependency Scanning,Vulnerability Management,Security Compliance Automation,OWASP Top 10,GDPR Compliance,HIPAA Compliance,PCI DSS Compliance,ISO 27001 Compliance,SOC 2 Compliance,FedRAMP Compliance,Cloud Governance,Tagging Strategies,Cost Allocation Tags,AWS Organizations Service Control Policies (SCPs),Azure Policy Assignments,Cloud Resource Groups,Cloud Cost Optimization Tools,Cost Explorer,Azure Advisor,Reserved Capacity Planning,Budget Alerts,Cost Allocation Reports,Serverless Cost Optimization,Cold Start Mitigation,Provisioned Concurrency,Right Sizing,Resource Tagging,DevOps Culture,Collaboration Tools,ChatOps,Incident Management,Service Level Objectives (SLOs),Service Level Indicators (SLIs),Error Budgets,Observability,Distributed Tracing,Cloud-Native Logging,Log Aggregation,Log Retention Policies,Real-Time Monitoring,Alerting,Anomaly Detection,Self-Healing Systems,Resilience Engineering,Chaos Monkey,GameDay Exercises,Circuit Breaker Design Pattern,Canary Testing,Rolling Deployments,Blue-Green Deployments,Infrastructure Testing,Automated Testing,Unit Testing,Integration Testing,Load Testing,Performance Testing,Security Testing,Compliance Testing,Chaos Engineering Experiments,Failure Injection Testing,Disaster Recovery Testing,Canary Analysis Tools,Canary Release Strategies,Deployment Automation,Infrastructure Automation,Configuration Management,Change Management,Release Management,Version Control,Git,GitHub,GitLab,Bitbucket,Versioning Strategies,Continuous Feedback,Post-Incident Reviews,Blameless Culture,Root Cause Analysis,Change Advisory Board (CAB),Agile Development,Scrum,Kanban,Lean Development,Sprint Planning,Backlog Refinement,Sprint Review,Daily Standups,Agile Metrics,Velocity,Lead Time,Cycle Time,Work In Progress (WIP),Cumulative Flow Diagram (CFD),Sprint Burndown Chart,Story Points,Fibonacci Sequence,Planning Poker,Estimation Techniques,Cross-Functional Teams,Continuous Learning,Communities of Practice,Knowledge Sharing,Lunch and Learns,Technical Debt,Refactoring Techniques,Code Smells
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