Hiring 3 AI Engineers: How a Series B HealthTech startup in Boston Scaled with Second Talent

The Challenge

The Boston-based HealthTech company reached $8M in annual recurring revenue by mid-2025. Their core product needed a major upgrade. Clinical partners demanded faster, more accurate diagnostic predictions. The existing rule-based system could not scale.

The technical requirements were specific. They needed engineers with 7+ years of experience in production machine learning. PyTorch and TensorFlow expertise was mandatory. The team had to understand healthcare data constraints and HIPAA compliance. Most importantly, they needed people who could ship fast.

Local hiring in Boston proved impossible. Senior AI engineers with healthcare experience commanded $220K to $280K in base salary. Stock options added another 20% to 30%. The startup’s Series B runway could not support three hires at that rate. Recruiting timelines stretched to 4 months per role. They had 6 months to deliver the new engine to clinical partners.

Offshore options looked risky. The CTO interviewed 12 candidates from various platforms. Most lacked production ML experience. None had worked with healthcare data at scale. The team needed senior engineers who could architect systems independently, not junior developers requiring supervision.

The Solution

Second Talent presented three AI engineers in Vietnam within 11 days. Each had 7+ years building production ML systems. All three had deployed models processing sensitive data in regulated industries. Their GitHub profiles showed contributions to open-source ML frameworks. Technical interviews confirmed deep knowledge of model optimization and deployment pipelines.

The hiring process moved fast. Second Talent pre-screened candidates against the specific tech stack: PyTorch for model development, TensorFlow for production deployment, Docker and Kubernetes for orchestration. Each engineer completed a paid technical assessment building a small diagnostic classifier. The CTO saw working code before making offers.

Second Talent handled the EOR setup in Vietnam. Contracts were compliant with local labor law. Payroll ran through Second Talent’s infrastructure. The engineers received $6,200 to $7,800 monthly, competitive rates for senior AI talent in Vietnam. Health insurance, equipment allowances, and professional development budgets were included. The startup paid a single consolidated invoice.

The three engineers started within 18 days of the first interview. Second Talent provided onboarding support for the first two weeks. The team integrated into daily standups immediately. Time zone overlap gave 4 hours of real-time collaboration each day. The engineers adopted the startup’s AI-native workflow: Claude for code review, GitHub Copilot for boilerplate, custom GPT agents for documentation.

The Results

The diagnostic prediction engine went to production in 10 weeks. The team built 14 different model architectures and tested them against 280,000 anonymized patient records. The final ensemble model achieved 94% accuracy on the validation set, exceeding the 90% target. Inference latency dropped from 840 milliseconds to 340 milliseconds, a 60% reduction that made real-time predictions viable in clinical workflows.

Cost savings were immediate and substantial. The three engineers cost $22,800 monthly including Second Talent’s fees. Equivalent US hires would have cost $63,000 monthly in salary alone, before benefits and equity. The annual savings reached $240,000. The startup redeployed that budget to hire two additional product managers and extend their runway by 7 months.

Productivity metrics exceeded internal benchmarks. The team shipped 47 pull requests in the first month. Code review turnaround averaged 3.2 hours. The engineers required minimal supervision after week three. By week eight, they were proposing architecture improvements that reduced cloud infrastructure costs by $1,840 monthly. The CTO noted that AI-native development practices made the remote team as productive as co-located engineers would have been.

“We needed senior AI engineers who could architect and ship independently. Second Talent delivered three engineers with real production ML experience in under three weeks. They built our diagnostic engine in 10 weeks and saved us $240K annually. The EOR service meant we focused on building product instead of international compliance.”

CTO, Series B HealthTech Startup