Hey founders! If you’re building a tech startup, you’ve probably realized that AI talent is crucial for your success. But here’s the tricky part: should you hire an AI engineer or a machine learning engineer? While these roles might sound similar, making the wrong choice could cost you precious time and runway.
The TL;DR for Busy Founders
Let’s cut to the chase: AI engineers typically focus on building and deploying complete AI systems, while machine learning engineers zero in on developing and optimizing specific ML models. Think of AI engineers as architects who design the whole building, while ML engineers are the experts who perfect each room’s functionality.
Breaking Down the Roles
The AI Engineer’s Playbook
Think of AI engineers as full-stack AI specialists. They’re the ones making sure AI actually works in the real world and not just in research papers. Here’s what they do:
- Design and implement end-to-end AI systems – From brainstorming to deployment, they build AI solutions that solve real problems.
- Handle integration with existing tech stacks – AI needs to play nice with your current software, and AI engineers make sure that happens.
- Work on multiple types of AI – Whether it’s natural language processing (NLP) (think chatbots and voice assistants) or computer vision (self-driving cars, facial recognition), they cover a broad range of AI applications.
- Focus on production-ready AI solutions – It’s not just about research; they build AI that can actually be used by businesses.
- Tackle both technical and business requirements – AI isn’t just about cool algorithms; it needs to align with business goals and make sense financially.
Salary Insight: AI engineers are in high demand, and it shows in their paychecks. A Builtin report states that AI engineers command a median salary of $180,000-$220,000.
The Machine Learning Engineer’s Domain
ML engineers dive deep into the data science trenches. While AI engineers focus on the full system, ML engineers zoom in on the models and algorithms that make AI tick. Their typical day includes:
- Building and optimizing ML models – They develop machine learning models that analyze data and make predictions.
- Feature engineering and selection – Figuring out which data points matter most for a model’s performance.
- Training and testing algorithms – Running models through massive amounts of data to ensure accuracy.
- Fine-tuning model performance – Tweaking models to make them faster, smarter, and more efficient.
- Working closely with data scientists – Data scientists create the initial models; ML engineers make them work at scale.
Salary Insight: ML engineers also earn hefty salaries. According to Builtin, they typically earn $155,000-$210,000.
Check out our Tech Salary Guide for Startups.
When to Hire Each Role
Hire an AI Engineer When:
AI engineers are your go-to if you’re making AI a core part of your product. They’re the big-picture builders who make sure AI fits seamlessly into your startup. Bring one on board if:
- You need to build an AI-first product – If your startup revolves around AI (think AI-powered SaaS, smart assistants, or autonomous systems), an AI engineer is a must.
- Your startup requires multiple AI capabilities – Need computer vision, NLP, and recommendation systems all in one place? AI engineers handle diverse AI use cases rather than just one niche.
- You want to integrate AI across your tech stack – If AI needs to work smoothly with your existing software, cloud infrastructure, or backend systems, an AI engineer can make that happen.
- You need someone who can bridge technical and business requirements – AI is useless if it doesn’t align with your business goals. AI engineers understand both the tech side (building models) and the business side (making sure AI actually drives revenue or efficiency).
Hire a Machine Learning Engineer When:
ML engineers are the data-crunching, model-tweaking pros who make sure your AI predictions are sharp and reliable. You’ll want one on your team if:
- You have specific ML use cases – Need to build a fraud detection system, a recommendation engine, or demand forecasting models? ML engineers are the ones who turn raw data into high-performance machine learning models.
- You need to optimize existing models – If your AI product is already up and running but isn’t performing at its best, an ML engineer can fine-tune the models for better accuracy, efficiency, and speed.
- You’re focused on data-driven predictions – ML engineers specialize in pattern recognition and predictive analytics, making them a perfect fit if you’re trying to forecast trends, detect anomalies, or personalize user experiences.
- You already have a strong data science team – Data scientists create prototypes and initial models, but ML engineers scale them for production and ensure they work efficiently in real-world applications.
Bottom Line: If your startup is AI-heavy, you need an AI engineer to build and manage full AI systems. If you’re focused on refining machine learning models, go for an ML engineer.
The Hybrid Reality: AI ML Engineering
Here’s something interesting: Talent partners note a growing trend of “AI ML engineers,” professionals who combine both skill sets. These hybrid roles are particularly valuable for early-stage startups where one person might need to wear multiple hats.
Real Talk: What This Means for Your Startup
Early-stage startups often benefit more from hiring AI engineers first. Why? They can handle both high-level system design and get their hands dirty with implementation. As your startup grows, you can bring in ML engineers to optimize specific components.
The Skills Breakdown
AI Engineer Must-Haves:
- System architecture expertise – They don’t just code; they design how AI fits into the entire tech ecosystem, making sure everything runs smoothly and efficiently.
- Multiple AI framework proficiency – Whether it’s TensorFlow, PyTorch, or OpenCV, they know their way around the top AI libraries and can pick the right tool for the job.
- Production deployment experience – It’s one thing to build an AI model, but getting it to work at scale, handling real-world data, and staying reliable? That’s what AI engineers do best.
- Business requirement translation – AI isn’t just about cool tech, it has to solve actual business problems. AI engineers know how to take high-level company goals and turn them into functional AI systems.
- Cross-functional collaboration skills – AI doesn’t exist in a vacuum. AI engineers work with software developers, product managers, and even marketing teams to make sure AI solutions fit seamlessly into the company’s workflow.
Machine Learning Engineer Must-Haves:
- Deep mathematical background – Machine learning is built on linear algebra, probability, and statistics. ML engineers use these skills to create and refine powerful models.
- Advanced ML algorithm knowledge – From gradient boosting to deep learning, they know how to choose and tweak the right algorithm for any given problem.
- Data modeling expertise – Good models need good data. ML engineers understand how to structure, clean, and preprocess data to make sure models perform at their peak.
- Performance optimization skills – AI models can be slow and expensive if they aren’t fine-tuned. ML engineers optimize models to run faster, use fewer resources, and still maintain accuracy.
- Strong experimentation mindset – The first model is rarely the best one. ML engineers are always running A/B tests, tweaking hyperparameters, and trying new approaches to get the best results.
Making the Right Choice
Start by answering these questions:
Hiring the right AI talent isn’t just about picking a job title. It’s about making sure they fit your startup’s actual needs. Before making a decision, ask yourself these key questions:
- What’s your primary technical need?
Are you looking to build an entire AI system from the ground up, or do you just need someone to refine machine learning models? If it’s the first, you need an AI engineer. If it’s the latter, an ML engineer is the better fit.
- How mature is your AI strategy?
If you’re still in the early stages and figuring out how AI fits into your product, you’ll want an AI engineer who can architect the whole thing. But if you already have AI in place and need to boost accuracy, efficiency, or scalability, an ML engineer can fine-tune your existing models.
- What’s your current team composition?
Do you already have data scientists, software engineers, or DevOps specialists? If so, an ML engineer can work alongside them to build smarter models. But if you’re lacking AI expertise altogether, an AI engineer can lay the technical foundation and integrate AI into your stack.
- What’s your budget and timeline?
AI engineers tend to cost more since they handle broader, system-level AI work. ML engineers, while still expensive, may be a more cost-effective choice if you only need help with model training and optimization.Also, consider your timeline because AI projects take time. Hiring the right person upfront can save you months of trial and error.
Further Insights:
How to Hire Machine Learning Engineers and Data Scientists
Looking Ahead
The distinction between AI and ML engineering roles continues to evolve. As we’ve seen from recent OpenAI and Anthropic job postings, many companies now seek professionals with overlapping skill sets, creating a new category of AI ML engineering roles.
Bottom line: Choose based on your immediate needs, but keep an eye on the growing trend of hybrid roles. Your early hiring decisions will shape your AI capabilities for years to come.
