My first exposure to hiring AI was at Uber in 2017. At the time, we were part of a small subset of companies recruiting specialized Machine Learning talent to build custom models and AI-driven applications.
In the eight years since, the demand for AI talent has exploded. What was once the domain of tech giants and research labs is now a fundamental need across startups of all stages and sizes. At Pear, over 70% of open roles across companies require some level of AI expertise. Founders aren’t just hiring Machine Learning Engineers anymore—they’re building teams that include Researchers, MLOps Engineers, and Product Engineers to drive their core products forward.
Through working with hundreds of founders, I’ve observed that many struggle not just with finding AI talent, but understanding what talent they actually need. Even the most technical founders are often unsure of where to start. In this blog we’ll explore the critical roles that define AI teams, when to hire them, and what you need to know about the talent market before you begin your search.
The AI production lifecycle
AI hiring isn’t one-size-fits-all. The right hires depend on your product roadmap, technical complexity, and long-term AI strategy. Before deciding who to hire, you first need to determine what AI work actually needs to be done.
The table below breaks down the AI production lifecycle into four core phases: Ideate, Build, Deploy, and Scale. Each phase represents a critical milestone in AI model development and requires a different set of skills.
Not every company progresses through all four stages of development. Many companies leverage pre-trained models or third-party tools to power their products, meaning their hiring needs are concentrated in the “Deploy” and “Scale” phases.
The key is understanding which phases are essential to your business and structuring your hiring strategy accordingly.
| Phase | What Happens | Key Role (if needed) |
| Research | Research and explore new AI approaches, exploring novel algorithms and solutions | AI Research Scientist (only if solving novel AI problems) |
Build | Develop, train, and optimize machine learning models | Machine Learning Engineer (if developing proprietary models) |
Deploy | Integrate models into applications and ensure operational readiness | AI Engineers (for companies bringing AI Into production) |
Scale | Maintain, monitor, and optimize AI systems for long-term scalability and reliability | MLOps Engineer (once scaling becomes a priority) |
The AI talent ecosystem
Now that we’ve identified the AI roles you need to fill, you can use the below guide to dive deeper into each role type, when, and how to approach your search:
AI Research Scientist
Focused on developing new AI algorithms and exploring innovative applications, Research Scientists operate at the cutting edge of the field. This role is more theoretical and academic, making it ideal for startups looking to solve deeply technical, novel problems.
When to hire for this role:
- Most early-stage companies do not need to hire Research Scientists.
- Companies that do are building their own proprietary AI models
- These companies are addressing highly novel and deeply technical problems that cannot be solved through standard AI practices.
- Examples include startups building in research-heavy domains like robotics, advanced NLP, or generative AI.
What to know about the talent market:
- The talent pool for Research Scientists is traditionally concentrated in academia, large tech companies, and research labs.
- The title “Research Scientist” historically required a PhD, but as large tech companies and graduate programs expand their R&D departments, the role is becoming more accessible to candidates with bachelor’s and master’s degrees,
- Candidates hold advanced degrees in AI-related fields, such as computer science, mathematics, or physics.
- Most Researchers seek the opportunity for “intellectual ownership”; meaning they can publish papers, present at conferences, and have their contributions recognized within and beyond the company.
Related job titles: Applied Scientist, Machine Learning Researcher, Deep Learning Scientist, Computer Vision Researcher, NLP Researcher
Machine Learning Engineer
Machine Learning Engineers (MLEs) bridge the gap between Research Scientists, Data Scientists, and Software Engineers. They specialize in developing, training, and deploying machine learning models while ensuring they are scalable, efficient, and production-ready. MLEs possess the technical expertise to build their own AI models and the engineering skills to push them into production environments.
When to hire for this role:
- Companies building their own AI models
- When your product requires a combination of model development and deployment
- Critical for startups in the early to mid stages of building AI-driven applications
- Hiring MLEs can be a strategic solution for companies that are resource-constrained but need to fill gaps in model development and software engineering
What to know about the talent market:
- Demand for ML Engineers has skyrocketed, making this one of the most competitive roles.
- Look for candidates with experience in end-to-end model development, including data preprocessing, model optimization, and deployment.
- Hiring for mid-level talent (~2-4 yrs) can be a strategic move, as senior candidates are often in high demand at large tech companies.
Related job titles: Research Engineer, Deep Learning Engineer, NLP Engineer, Computer Vision Engineer, Data Engineer (with ML Focus)
AI Engineer
AI Engineers are Software Engineers who integrate models into your existing systems and applications. They are versatile, hands-on generalists who build full-stack AI applications, maintain AI systems, and optimize model performance. AI Engineers are not responsible for model development. They work closely with Research Scientists and ML Engineers to implement models or work independently with open-source tools to build end-to-end AI applications.
When to hire for this role:
- Once the AI model is trained and ready to be deployed into your product.
- Ideal for startups leveraging open-source AI models and technologies to build products.
- AI Engineers are fundamental to any company building an AI product, with few exceptions (e.g., enterprise infrastructure companies).
What to know about the talent market:
- Most full-stack software engineers interested in AI can adapt to this role.
- Hiring software generalists and developing them into AI Engineers is often a faster and more cost-effective approach than hiring Machine Learning Engineers or Research Scientists.
- Prioritize candidates with existing experience in AI integration (e.g., using APIs, frameworks like LlamaIndex or LangChain).
- Seek candidates with a strong understanding of system design and distributed computing.
Related job titles: Any full-stack, backend, or product engineers with an AI/ML focus.
MLOps Engineer
MLOps Engineers combine the principles of DevOps with machine learning, focusing on the smooth deployment and management of AI models in production. They ensure the infrastructure and pipelines supporting your AI systems are robust, scalable, and reliable.
When to hire for this role:
- Hiring an MLOps Engineer too early can lead to inefficiencies—most early-stage startups don’t have the volume of models or the scale to justify a dedicated MLOps hire.
- However, delaying this hire too long can result in production bottlenecks, system downtime, or suboptimal performance that hinders growth. Timing is key!
- Founders must critically assess their current operational challenges and weigh whether adding this role is more of a “nice to have” or will directly impact their ability to scale.
What to know about the talent market:
- MLOps is a newer field, so finding experienced professionals can be challenging.
- Similar to how Software Engineers transition into AI Engineers, many candidates from DevOps or Data Engineering backgrounds transition into MLOps
- Look for candidates with skills in:
- Building machine learning platforms
- Developer tooling (CI/CD, Kubernetes)
- Cloud platforms (AWS, GCP, Azure)
Related job titles: DataOps Engineer, AI Infrastructure Engineer, ML Platform Engineer, AI Systems Engineer
As the AI talent landscape continues to evolve, founders must develop a highly-informed approach to building their teams. At Pear VC, we help founders navigate these complexities, ensuring their first AI hires are not just highly skilled but strategically aligned with their vision and goals.