How to Write a Machine Learning Engineer Job Description That Attracts Top Talent (+ Template)

Need a machine learning engineer job description you can post today? 

This guide shows you how to write a job description that attracts engineers who ship models to production and helps unqualified applicants self-select out, so you avoid applications from generalists with light ML exposure.

We’ve included best practices, exactly what to include, common mistakes to avoid, and a customizable template. 

Why Job Descriptions Matter More Than You Think

A well-written machine learning engineer job description is your competitive advantage in attracting the right technical talent while filtering out unqualified candidates. 

Sets clear technical expectations

ML engineers want to know exactly what they’ll be building before they apply. Generic descriptions like “develop AI solutions” tell them nothing about your data infrastructure, model complexity, or production requirements.

When you’re specific about technical challenges—“build recommendation systems handling 10M+ daily interactions” or “optimize NLP models for sub-100ms inference”—you attract candidates who’ve actually solved those problems before.

Demonstrates your AI maturity

How you describe the role signals how well you understand machine learning. If your job description mentions “AI” but doesn’t specify your tech stack, data pipeline, or model deployment process, experienced ML engineers will assume you’re just jumping on the AI bandwagon without real technical depth.

Speeds up your hiring process

A clear job description helps ensure the people who apply can actually do the complex technical work, reducing time spent on interviews that reveal fundamental skill gaps. 

When candidates can quickly assess whether they’re qualified for your specific ML challenges, you get better quality applications and faster hiring decisions.

What Makes a Great Job Description for ML Roles?

The difference between good and great ML job descriptions comes down to technical specificity and understanding what actually motivates top-tier ML and AI talent.

Technical clarity without overwhelming detail

Great job descriptions mention specific technologies and frameworks, but they don’t list every tool in your stack. Focus on what they’ll use daily: “You’ll primarily work with PyTorch and TensorFlow, building computer vision models for our autonomous vehicle platform” is more helpful than a twenty-item technology list.

Honest representation of your ML environment

If you’re a startup where you’re still figuring out your data strategy, say that. If you’re an established company with mature MLOps practices and production systems at scale, mention that too. Misrepresenting your technical maturity leads to early turnover when reality doesn’t match expectations.

Lead with the technical challenge

Strong ML engineers are motivated by interesting problems. Start your job description by highlighting what makes the work technically compelling:

“We’re building ML models that need to process 50TB of real-time data while maintaining sub-100ms inference latency. You’ll architect solutions for distributed training challenges that most ML engineers never encounter.”

This immediately attracts candidates who want to work on meaningful technical problems rather than routine model tuning.

Be specific about your ML infrastructure

Rather than generic statements about “cutting-edge technology,” describe your actual technical environment:

“Our ML platform handles model training on Kubernetes clusters with 100+ GPUs, automated hyperparameter tuning, and real-time model monitoring with automatic rollback capabilities.”

This helps candidates assess whether your infrastructure matches their experience level and interests.

Highlight business impact and scale

Top ML professionals want to build systems that matter. Connect their technical work to business outcomes:

“The recommendation models you build will directly impact how 10 million users discover content, with a 1% improvement in model accuracy translating to $2M in additional revenue.”

Show growth opportunities in emerging ML areas

Machine learning evolves rapidly, and the best candidates want to stay current. Be specific about learning opportunities:

“You’ll have opportunities to experiment with large language models, contribute to our open-source ML tools, and present your work at conferences like NeurIPS and ICML.”

What to Include in an ML/AI Job Description

Understanding what sections to include and how to write them effectively can make the difference between attracting top ML talent and getting lost in the noise of generic AI job posts.

Here are the essential components that make ML/AI job descriptions effective:

Job title

Your title needs to be both searchable and specific to the type of ML work you’re hiring for. “Machine Learning Engineer,” “AI Developer,” “Data Scientist,” or “ML Research Scientist” work better than creative titles.

If the role has specific focus areas, include those: “Computer Vision Engineer,” “NLP Research Scientist,” or “MLOps Engineer.” This helps qualified candidates find you while helping others self-select out.

Summary of the role

Think of this as your elevator pitch to potential ML candidates. In 2–3 sentences, capture what makes this AI opportunity compelling:

• What type of ML problems they’ll be solving and why it matters
• How they’ll fit into your existing data science team
• What makes this opportunity different from other ML roles

For example: “Join our ML team to build the recommendation engine that helps 50,000+ e-commerce businesses increase their conversion rates. You’ll own the full ML lifecycle from data exploration through production deployment, working with clean datasets and established MLOps infrastructure. This role is perfect for an experienced ML engineer who wants to see their models drive real business impact at scale.”

This immediately tells candidates about the problem domain, data quality, infrastructure maturity, and business impact.

Key responsibilities

Be specific about the actual technical work, not generic ML tasks. Experienced ML professionals want to understand the complexity and scope of what they’ll be building.

Good examples:

• Design and implement deep learning models for image classification using PyTorch, handling datasets with 10M+ labeled examples
• Build and maintain real-time inference pipelines serving 100k+ predictions per minute with sub-200ms latency requirements
• Collaborate with data engineering team to design feature stores and manage model training datasets in AWS/GCP
• Conduct A/B experiments to measure model performance impact on key business metrics
• Optimize model performance for production deployment, including quantization and edge deployment strategies

Avoid vague statements like “develop AI solutions” or “work with big data.” Be concrete about what they’ll actually be building and deploying.

Required skills and qualifications

This is where many companies go wrong by listing every ML technique they’ve ever heard of. Our recruiters always recommend focusing on what’s truly essential for success in your specific ML challenges.

Essential qualifications and ML skills typically include:

• Specific years of experience with your primary programming languages (Python, R, Java)
• Required ML framework proficiency (TensorFlow, PyTorch, scikit-learn)
• Core statistical and mathematical knowledge areas
• Experience with your deployment environment (cloud platforms, containerization)
• Domain-specific ML experience if truly necessary for your product

One of our recruiters notes:

Often specific industry knowledge can be easily taught. However, it’s often overvalued by companies compared to more critical core skills. 

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Preferred qualifications

This is where you can mention advanced skills that would help someone excel but aren’t deal-breakers:

• Experience with specific ML domains (computer vision, NLP, reinforcement learning)
• Advanced degrees in relevant fields
• Experience with specialized tools in your stack
• Publications or contributions to open-source ML projects
• Experience with MLOps tools and model monitoring

Salary range

ML and AI professionals expect transparency about compensation, especially given the wide salary ranges in the field. Include both base salary and any equity or bonus structures:

• Base salary range with experience level clarifications
• Equity compensation if applicable
• Performance bonus structures
• Typical total compensation for someone hitting expectations

For example: “Base salary: $120,000–$180,000 depending on experience level. Plus equity package and annual performance bonus up to 20% of base salary.”

Including salary information isn’t just helpful. It’s becoming legally required in many states and shows you’re serious about competing for top ML talent.

Research from the Society for Human Resource Management shows that organizations listing pay ranges report increased applications, making salary transparency a competitive advantage.

Technical stack and infrastructure

Give ML professionals a clear picture of their technical environment:

• Primary programming languages and ML frameworks
• Cloud platforms and infrastructure (AWS, GCP, Azure)
• Data storage and processing systems (data lakes, warehouses, streaming)
• MLOps tools for model deployment and monitoring
• Development and experimentation environments

Focus on the core technologies they’ll use daily rather than creating an exhaustive list of every tool in your environment.

Location and work setup

Be explicit about where ML work gets done, especially since many tech roles can be performed remotely:

• For on-site roles: Include the specific city and office location. Mention any flexibility for remote work during model training or research phases.
• For hybrid roles: Specify expectations for in-office time, especially for collaboration with product teams or access to specialized hardware.
• For remote roles: Clarify geographic restrictions and time zone requirements. Many ML teams need overlap for model reviews and experiment planning.

According to Stack Overflow’s 2024 Developer Survey, only 20% of developers work in an in-person setting. So it’s more likely than not that your new hire will be expecting at least a hybrid model if not fully remote. 

If you’re open to hiring remote ML talent, consider that expanding your search globally can access broader talent pools. Many top countries for offshore machine learning talent have professionals with strong technical skills and competitive compensation expectations.

Data and problem context

ML professionals want to understand the data they’ll work with and problems they’ll solve:

• Types and scale of datasets (structured, unstructured, real-time streams)
• Data quality and labeling processes
• Business problems their models will address
• Success metrics and performance expectations
• Integration with existing products or systems

Growth and learning opportunities

Top ML talent is motivated by continuous learning and technical growth. Mention:

• Opportunities to work with cutting-edge ML techniques
• Conference attendance and research publication support
• Collaboration with research teams or academic institutions
• Career advancement paths (senior ML engineer, research scientist, ML team lead)
• Access to specialized hardware (GPUs, TPUs) for experimentation

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Common Mistakes to Avoid in Your ML/AI Job Descriptions

These pitfalls can significantly hurt your ability to attract quality ML and AI talent:

Unrealistic skill combinations

Asking for “entry-level ML engineer with deep learning expertise” or “junior data scientist with production ML experience” signals that you don’t understand the ML talent market.

Focus on skills that match the complexity of work and compensation you’re offering. A junior role focused on data preprocessing and model evaluation doesn’t need the same expertise as a senior role architecting ML systems.

Focusing only on academic credentials

Companies often overemphasize advanced degrees when practical ML experience is more valuable. A candidate with a bachelor’s degree and three years building production ML systems often contributes more than someone with a PhD but no deployment experience.

Require competencies that actually predict success. According to research by SHRM, 

73% of organizations that eliminated degree requirements for certain positions reported finding at least one new hire they would have previously considered unqualified for the role.

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Limiting your search unnecessarily

If the role can be done remotely, restricting your candidate pool to one geographic area significantly reduces your options. This is especially problematic for specialized ML roles where local talent is scarce and expensive.

Machine learning is inherently location-independent, and opening up your search to global talent can dramatically increase your candidate pool while reducing hiring costs.

Many companies are discovering that hiring ML professionals in Latin America gives them access to talent with experience with US companies, strong English skills, and competitive technical abilities, while having salary expectations significantly below US market rates.

Working with ML engineering staffing companies that understand both the technical requirements and global talent landscape can help you find the right candidates faster.

Conversion Logix transformed their hiring approach by accessing talented professionals in LatAm with the help of Near, including an ML engineer. They are now saving $781,000 annually compared to hiring in the US.

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ML Engineer Job Description Template (Ready to Customize)

Machine Learning Engineer

About us: [Company Name] is [brief company description focusing on how you use ML/AI]. We’re [current stage, e.g., scaling our ML infrastructure, expanding our AI capabilities] and need a [experience level] machine learning engineer to [specific technical goal, e.g., optimize our recommendation systems, build our computer vision pipeline].

The role: We’re looking for an experienced ML engineer to [specific responsibility, e.g., lead our deep learning initiatives, build production ML pipelines, research new AI techniques]. You’ll work with [data types and scale, e.g., petabyte-scale user interaction data, real-time sensor streams, multi-modal content datasets] while [additional context, e.g., collaborating with our research team, scaling existing models, building from scratch]. This role is perfect for an ML professional who wants to [opportunity/growth potential] in a [technical environment description] environment.

What you’ll do:

• [Specific technical responsibility with scale, e.g., “Design and implement deep learning models for computer vision tasks, handling datasets with 50M+ labeled images”]
• [Infrastructure responsibility, e.g., “Build and maintain ML pipelines processing 10TB+ daily data using Apache Spark and Kubernetes”]
• [Optimization responsibility, e.g., “Optimize model inference performance to achieve sub-50ms latency for real-time predictions”]
• [Collaboration responsibility, e.g., “Work with product teams to define ML success metrics and design A/B experiments”]
• [Research responsibility, e.g., “Research and prototype new ML techniques, contributing to our technical blog and open-source projects”]
• [Additional 2-3 core responsibilities specific to your ML challenges]

What you’ll need:

• [Years of experience] years of hands-on machine learning experience
• Strong proficiency in [primary programming language, e.g., Python/R] and ML frameworks [e.g., TensorFlow, PyTorch]
• Experience with [specific ML domains relevant to your work, e.g., computer vision, NLP, time series forecasting]
• Knowledge of [statistical concepts, e.g., statistical modeling, experimental design, hypothesis testing]
• Experience with [deployment technologies, e.g., Docker, Kubernetes, cloud ML platforms]
• [Any domain-specific requirements that are truly necessary]

Nice to have:

• Advanced degree in [relevant fields, e.g., Computer Science, Statistics, Mathematics, or related field]
• Experience with [specialized tools, e.g., MLflow, Kubeflow, specific cloud ML services]
• Publications in [relevant venues, e.g., top-tier ML conferences or journals]
• [Open-source contributions or personal ML projects]
• Experience with [emerging technologies relevant to your roadmap]

Technical stack:

We primarily use: [list 5-7 core technologies they’ll work with daily, e.g., Python, PyTorch, AWS SageMaker, Apache Spark, Docker, Kubernetes, PostgreSQL]

Data and infrastructure:

• [Data types and scale, e.g., “Work with structured datasets (100M+ records) and unstructured data (images, text, audio)”]
• [Infrastructure scale, e.g., “ML training on distributed GPU clusters with automated hyperparameter tuning”]
• [Deployment environment, e.g., “Models deployed to production serving 1M+ predictions daily”]
• [Data quality, e.g., “Clean, well-labeled datasets with established data pipelines”]

Location & schedule:

• [Remote/Hybrid/On-site] position
• [Time zone requirements if applicable, e.g., “Core overlap hours with EST team”]
• [Any hardware access requirements, e.g., “Occasional on-site access for specialized GPU clusters”]
• [Collaboration expectations, e.g., “Weekly team meetings and quarterly research reviews”]

Compensation & benefits:

• Salary range: $[X,000 - Y,000] annually [based on experience/location]
• [Equity information if applicable, e.g., “Competitive equity package”]
• [Performance bonuses, e.g., “Annual performance bonus up to 20% of base salary”]
• [Key benefits: health insurance, learning budget, conference attendance, etc.]
• [ML-specific perks, e.g., “Access to latest ML hardware and cloud computing credits”]

Growth opportunities:

• [Technical advancement, e.g., “Lead ML research initiatives and mentor junior engineers”]
• [Learning support, e.g., “$5,000 annual budget for conferences, courses, and certifications”]
• [Research opportunities, e.g., “Time allocated for research projects and publication”]
• [Career progression, e.g., “Clear path to Senior/Staff ML Engineer or ML Research Scientist roles”]

How to apply: Send your resume and [additional requirements] to [email/application link]. Include:

• [Portfolio requirements, e.g., “Links to ML projects, GitHub repositories, or research publications”]
• [Specific information, e.g., “Brief description of your most challenging ML project and technical approach”]
• [Code samples if relevant, e.g., “Code samples demonstrating ML engineering best practices”]

Our hiring process includes: [Brief overview of interview stages, e.g., “Technical screen → ML system design → Code review → Final interview with ML team”]

Final Thoughts

You now have everything you need to write a machine learning engineer job description that attracts qualified professionals while filtering out candidates who lack the specialized knowledge required for production ML work.

By being clear about technical challenges, honest about your ML infrastructure maturity, and realistic about the skills needed for success, you’ll connect with ML engineers who can actually ship models to production rather than just build impressive demos.

But if you need to fill this role quickly and want to skip the weeks of posting across multiple platforms and screening hundreds of applications, there’s a faster path forward.

Rather than hoping the right ML engineer finds your posting among dozens of similar job descriptions, we can present you with three pre-vetted machine learning engineers within a week. These aren’t random applicants hoping to break into ML work. They’re experienced professionals who have already been screened for the specific technical skills and production experience you need.

You can interview top pre-vetted Latin American ML engineers for free with no commitment.

It’s a chance to see firsthand the caliber of professionals available while potentially solving your ML hiring challenge in days rather than months.

The talent shortage in machine learning is real, but it doesn’t have to slow down your AI initiatives. While other companies spend months competing for the same limited pool of local candidates, you could have your ML engineering role filled and your new hire building production systems within weeks.

Schedule a free consultation to discuss your specific ML hiring needs and see how quickly we can connect you with qualified candidates who are ready to drive your machine learning projects forward.