Healthcare · Series A Health-Tech Startup
Hired 15 AI/ML engineers for a health-tech scale-up
Specialised AI/ML engineering recruitment for a health-tech startup building diagnostic AI products, requiring rare domain + tech intersection skills.
- Python
- PyTorch
- TensorFlow
- MLOps
- FHIR/HL7
Engagement card
Series A Health-Tech Startup
- ML Engineers Placed
- 15
- Niche Skill Match
- 100%
- Avg. Time to Hire
- 18 days
- Duration
- 8 weeks
- Talpro team
- 2
Representative outcome · attributed on request
The situation
What the client walked in with.
A Series A health-tech startup building AI-powered diagnostic tools needed 15 ML engineers with a rare combination: deep learning expertise (PyTorch/TensorFlow) AND healthcare domain knowledge (FHIR standards, medical imaging, regulatory awareness). Generic recruiters couldn't differentiate between a generic ML engineer and one who understood healthcare data constraints.
What we ran
The engagement.
Talpro's specialist tech recruitment team partnered with the client's ML lead to build a custom assessment framework testing both ML depth and healthcare data understanding. We sourced from niche communities: healthcare AI research labs, medical imaging startups, and health informatics programs. Every candidate went through a take-home challenge using anonymised health data before shortlisting.
Representative outcome · attributed on request
What it left the client with.
- 15 ML engineers placed with 100% niche skill match
- Average time from requirement to accepted offer: 18 days
- All 15 hires passed the client's internal technical bar on first attempt
- 93% retention at 6 months in a high-attrition talent segment
- Client's AI product received CDSCO pre-submission clearance within 6 months of team formation