Hiring machine learning engineers is difficult for even well-established organizations with experienced HR teams. These roles require a complex skill set, so without technical expertise, it can be hard to tell who can actually build and deploy working models. You shouldn’t need to be an ML expert to make the right hire.
With Hunter, you don’t have to be. Our experienced STEM recruiters have spent years building and placing technical teams for global innovators. Hire up to 40% faster through a recruiting partner that understands both the talent market and the technology. Whether you’re scaling a data science team or launching new AI products, we help you find the engineers who keep your organization ahead of the curve.
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Hiring teams often say that recruiting machine learning engineers feels overwhelming. The roles are highly technical, job descriptions are difficult to define, and many qualified engineers are already focused on long-term product or research work. Building enough domain knowledge to evaluate candidates properly is important, but it can be unrealistic for teams managing multiple priorities.
Our machine learning engineer recruiters simplify that process. We collaborate with your hiring managers to define real-world outcomes, clarify requirements, and structure competitive offers that attract high-caliber engineers. Through our network of quiet-search professionals, we connect you with experienced, vetted candidates.
Discover experienced ML engineering talent not found on job boards, including:
Get in touch. We learn your technical needs, project goals, and hiring timeline to create a focused recruiting plan.
Our network includes vetted ML engineers ready to consider new opportunities.
From the first interview to onboarding, Hunter manages every step for a seamless placement.
Access to high-quality candidates – Includes 70% passive professionals you will not find on job boards.
Faster time-to-hire – Average 41 days from request to placement.
Better retention – 96% of placements stay 3+ years.
Flexible solutions – Permanent, contract, and project-based staffing.
Risk free – Guaranteed contingent search. Pay only when we deliver.
To support digital initiatives, a top insurance provider needed help hiring specialized technology professionals. Internal delays and communication gaps prompted them to partner with Hunter. Through full-cycle recruiting support, we delivered:
Days average time-to-fill
submit-to-interview ratio
key technology management and engineering roles to lead, scale, and secure the organization’s digital infrastructure.
Leverage our STEM staffing experts to strengthen your organization’s machine learning capabilities.
Add contract-based ML engineers for short-term or project-specific initiatives.
Recruit senior ML engineers and technical leads to shape enterprise AI strategy.
Deliver defined ML projects on schedule with outcome-based staffing solutions.
Simplify contingent workforce management with scalable staffing programs for ML initiatives.
Streamline onboarding, payroll, and benefits administration for contract ML engineers.
of tech candidates
go through Hunter’s two-step quality screening process for technical skills
Average time to fill
from the time a job is posted until a candidate is placed into the role.
of technology placements
made by Hunter remains with their employer for 3+ years.
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Core skills typically include proficiency in Python, experience with frameworks like TensorFlow or PyTorch, a strong foundation in math and statistics, and familiarity with data processing pipelines. For production roles, understanding MLOps, version control, and cloud deployment is equally important.
It may be time to hire when your team has more data than it can analyze manually, when you’re ready to build predictive or automation-focused systems, or when existing analytics can’t solve complex problems. You should also have defined use cases, access to relevant datasets, and enough infrastructure to support experimentation and deployment.
Demand for machine learning talent far exceeds supply. Many engineers are already engaged in long-term product or research work, and evaluating their abilities requires technical knowledge. Roles often combine software development, data science, and applied math, which makes screening and interviewing more complex than typical technical roles.
Work with engineering leads to define the real outcomes the role needs to achieve, rather than relying solely on technical jargon in job descriptions. You can also use technical assessments, coding challenges, or involve external staffing experts to help evaluate candidates.
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