Scaling DeepTech's AI Research Team

The Challenge

DeepTech AI, a Series B computer vision startup, was struggling to scale their research team to meet aggressive product development timelines. With $50M in fresh funding and ambitious goals to launch three new AI products within 12 months, they needed to rapidly expand from 8 to 20 AI researchers and engineers.

Key Challenges:

• Talent Shortage: Difficulty finding senior ML researchers with computer vision expertise
• Speed Requirements: Needed to hire 12 people in 8 weeks to meet product deadlines
• Quality Standards: Required PhD-level researchers and senior engineers from top companies
• Cultural Fit: Maintaining startup culture while bringing in experienced professionals
• Budget Constraints: Series B budget required cost-effective hiring solutions

Our Approach

We deployed our comprehensive AI talent acquisition methodology, leveraging our extensive network and data-driven matching process.

Discovery Phase (Week 1)

• Conducted stakeholder interviews with CTO, Head of Research, and key team leads
• Analyzed existing team composition and identified skill gaps
• Defined technical requirements for each role (ML Engineer, Research Scientist, Computer Vision Expert)
• Established cultural fit criteria and success metrics
• Created detailed candidate personas and sourcing strategy

Sourcing & Screening (Weeks 2-3)

• Activated our network of 2,500+ computer vision professionals
• Targeted researchers from top AI labs (Stanford AI Lab, MIT CSAIL, Google Research)
• Engaged passive candidates from FAANG companies and AI unicorns
• Conducted initial technical screens and cultural fit assessments
• Presented 45 qualified candidates across all roles

Evaluation & Selection (Weeks 4-5)

• Coordinated technical interviews with domain experts
• Facilitated culture fit sessions with existing team members
• Conducted reference checks with previous colleagues and supervisors
• Provided detailed candidate assessments and recommendations
• Supported client decision-making process

Closing & Onboarding (Week 6)

• Negotiated competitive offers aligned with market rates
• Managed counteroffers and candidate concerns
• Coordinated start dates and onboarding logistics
• Provided onboarding support and 30-day check-ins

Results

Quantitative Impact

• 12 successful hires across 4 different AI specializations
• 6-week timeline - 2 weeks ahead of the original 8-week target
• 65% cost savings compared to traditional executive search firms
• 100% offer acceptance rate with zero counteroffers lost
• 45 qualified candidates sourced and screened

Qualitative Outcomes

• Team Velocity: Development speed increased by 200% within 3 months
• Product Innovation: Successfully launched 2 new AI products ahead of schedule
• Cultural Integration: All new hires rated 9/10 for cultural fit in 90-day reviews
• Retention: 100% retention rate at 12-month mark
• Investor Confidence: Successful team scaling contributed to Series C fundraising

Candidate Breakdown

Research Scientists (4 hires)

• 2 from top university AI labs (Stanford, MIT)
• 1 from Google Research (computer vision team)
• 1 from OpenAI (multimodal AI research)

Senior ML Engineers (5 hires)

• 2 from Tesla (Autopilot team)
• 1 from Meta (Reality Labs)
• 1 from NVIDIA (AI infrastructure)
• 1 from Waymo (perception systems)

Computer Vision Specialists (3 hires)

• 1 from Apple (Vision Pro team)
• 1 from Amazon (Alexa vision)
• 1 from Microsoft Research (computer vision)

Client Testimonial

"Sherpa transformed our hiring process completely. In 6 weeks, they delivered a world-class research team that would have taken us 6+ months to build ourselves. The quality of candidates was exceptional - every single hire has exceeded our expectations. The team they helped us build was instrumental in our successful Series C raise."

Sarah Martinez, CTO at DeepTech AI

Key Success Factors

1. Deep Network Access: Leveraged relationships with AI researchers and engineers globally
2. Technical Expertise: Our team's AI background enabled accurate candidate assessment
3. Speed & Efficiency: Parallel processing and dedicated resources accelerated timeline
4. Cultural Alignment: Thorough cultural fit assessment ensured team cohesion
5. Market Knowledge: Competitive intelligence enabled successful offer negotiations

Long-term Impact

Six months after the successful placements:

• DeepTech AI raised a $120M Series C round
• The research team published 8 papers at top AI conferences
• Two new AI products launched with significant market traction
• Company valuation increased by 300%
• Team expanded further to 35 AI professionals

Ready to scale your AI team like DeepTech? Contact us to discuss your hiring needs.