Technical Team Build

Building a Complete ML Engineering Team

How we assembled an 8-person machine learning engineering team for a healthcare AI company in just 6 weeks.

6 Weeks
Total Timeline
8 Hires
Team Members
Healthcare
Industry
100%
Success Rate

The Challenge

A healthcare AI startup had just secured FDA approval for their diagnostic imaging platform and needed to rapidly scale their engineering team to meet enterprise demand. They required:

  • Senior ML engineers with healthcare/medical imaging experience
  • Engineers familiar with FDA regulatory requirements
  • Expertise in computer vision and deep learning
  • Experience with HIPAA-compliant systems

⚠️ Challenge: Healthcare AI talent is extremely scarce, with most candidates already employed at major tech companies or established medical device companies.

Target Team Composition

Senior Roles (4 positions)
  • • Senior ML Engineer - Computer Vision
  • • Senior ML Engineer - NLP/Clinical Data
  • • ML Infrastructure Engineer
  • • Senior Data Scientist - Clinical Research
Mid-Level Roles (4 positions)
  • • ML Engineer - Model Deployment
  • • ML Engineer - Data Pipeline
  • • Software Engineer - ML Platform
  • • Data Engineer - Healthcare Systems

Our Multi-Channel Strategy

1
Healthcare Network Activation

Leveraged our specialized healthcare AI network, including contacts at major medical device companies, research hospitals, and healthcare-focused AI startups.

2
Academic Partnerships

Reached out to top medical AI research labs at universities, targeting PhD graduates and postdocs looking to transition to industry roles.

3
Parallel Hiring Tracks

Ran simultaneous hiring processes for all 8 positions, with coordinated interview schedules to ensure team chemistry and complementary skill sets.

Week-by-Week Progress

Weeks 1-2: Sourcing & Initial Outreach

Activities:

  • • Identified 120+ potential candidates
  • • Conducted 45 initial phone screens
  • • Shortlisted 24 candidates for client review

Results:

  • • 18 candidates advanced to technical interviews
  • • 100% response rate from target candidates
  • • Strong pipeline for all 8 positions
Weeks 3-4: Technical Interviews & Assessment

Activities:

  • • 18 technical interviews conducted
  • • Healthcare-specific case studies
  • • Team fit assessments

Results:

  • • 12 candidates moved to final rounds
  • • Strong technical competency across all roles
  • • Excellent cultural alignment
Weeks 5-6: Final Interviews & Offers

Activities:

  • • 12 final interviews with leadership team
  • • Reference checks and background verification
  • • Coordinated offer negotiations

Results:

  • • 8 offers extended and accepted
  • • 100% offer acceptance rate
  • • All hires started within 2 weeks

Team Composition & Results

Successfully Placed Team

Senior Hires

  • Former Google Health ML Engineer (Computer Vision Lead)
  • Ex-IBM Watson Health NLP Specialist
  • Former Philips Healthcare ML Infrastructure Engineer
  • PhD from Stanford Medical AI Lab

Mid-Level Hires

  • Former Siemens Healthineers ML Engineer
  • Ex-Epic Systems Data Pipeline Engineer
  • Former Tempus ML Platform Engineer
  • Ex-Cerner Healthcare Data Engineer

6-Month Impact

Product Development
  • Launched 2 new diagnostic modules
  • Improved model accuracy by 25%
  • Reduced inference time by 60%
Business Growth
  • Signed 15 new hospital partnerships
  • Revenue increased 400%
  • Secured Series B funding
Team Success
  • 100% team retention rate
  • 3 internal promotions
  • Team expanded to 15 members
"

"Building our entire ML team through Vellan Partners was the best decision we made. They understood the unique challenges of healthcare AI and found us candidates who were not just technically excellent, but also passionate about improving patient outcomes."

CTO & Co-Founder

Healthcare AI Startup

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