Senior Machine Learning Engineer

Our Story:

Unlocking the Ageless Revolution for Patients and Practices:

With 2 million users, over $2 billion generated revenue, and ranking among the Top 200 apps globally, RepeatMD is leading the charge as we expand into new verticals.

Today, as a B2B SaaS company in the Aesthetics and Wellness Industry, we proudly serve 4000+ practices across all 50 states and Canada.

We are looking for those who are passionate to join our mission!

About the Role

We’re building the next generation of AI-powered facial analysis and rejuvenation previews in the health and beauty space. Our goal is to give over 2 million users the ability to see what they could look like with aesthetic and wellness treatments before they ever step into a clinic.

You’ll help bring this vision to life. 

You’ll design and optimize computer vision models that transform real patient images into realistic, medically accurate “after” results. You’ll work closely with product, design, and medical experts to train, fine-tune, and deploy models that help people make confident, informed decisions about their appearance and well-being.

This role is ideal for someone who’s passionate about AI imaging, model training, and generative beauty tech and wants to build something that will redefine how people experience aesthetic and wellness care.

• Inherit, audit, and either harden or replace our current PoC. Your call, backed by data.
• Design a model platform that supports rapid hot-swapping, A/B testing, and instant rollback.
• Balance server GPU today with a practical path to partial on-device inference for privacy and latency.
  
  

What You'll Do

Model Development & Research

• Evaluate and benchmark state-of-the-art models (segmentation, detection, SDXL/diffusion, LLM-based classifiers)
• Select the best architectures balancing accuracy, inference speed, efficiency, and fairness
• Develop generative models for before/after treatment previews, ensuring realism, inclusivity, and clinical credibility

Scalability & Optimization

• Design inference pipelines optimized for low latency and high throughput
• Use model compression, quantization, and distillation to reduce compute costs while maintaining accuracy
• Leverage AWS (Lambda, Step Functions, SageMaker, GPU pipelines) or equivalent to build cloud-scale systems

Reliability & Guardrails

• Build quality-control systems (blur detection, orientation/rotation correction, lighting checks)
• Implement multi-candidate generation + ranking for robustness
• Ensure identity consistency and similarity verification across generated images
• Develop fallback algorithms for resilience under failure modes

Production & MLOps

• Package models for deployment into production at scale, with proper versioning and monitoring
• Build CI/CD pipelines for continuous training, testing, and deployment
• Define and track key performance metrics (latency, throughput, drift, fairness, user satisfaction)

Collaboration & Impact

• Work with product, engineering, and clinical teams to align on accuracy, safety, and inclusivity goals
• Stay up-to-date with research, and bring the best of academia and industry into production
• Act as a thought partner in shaping responsible AI practices within the product
• Mentor a small pod of ML and Platform engineers as hiring ramps.

What Success Looks Like

• Model Performance: Maintain >95% accuracy on key facial analysis tasks while achieving <2s inference latency
• System Reliability: Achieve 99.5% uptime with proper fallback mechanisms
• Deployment Velocity: Ship model improvements to production within 2-week cycles
• Fairness Metrics: Ensure model performance parity across diverse demographic groups (±3% variance)
• Cost Efficiency: Reduce inference costs by 30% through optimization while maintaining quality standards

What We're Looking For

Required

• Master's/PhD in Computer Science, Machine Learning, or equivalent practical experience
• 5+ years of experience in applied ML, with a focus on computer vision and generative models
• Strong track record with segmentation, detection, and diffusion-based generative models (SDXL, Stable Diffusion, etc.)
• Deep expertise in optimizing ML for scalability and efficiency (quantization, pruning, distillation)
• Strong engineering background: Python, PyTorch/TensorFlow, clean production code
• Hands-on experience deploying ML at scale in AWS/GCP/Azure
• Proven ability to balance speed, accuracy, and cost in real-world deployments
• Comfort reading and integrating with a .NET and React stack via clean service contracts.

Preferred

• Experience in healthtech, medtech, or regulated AI environments
• Knowledge of bias mitigation and fairness techniques in ML
• Experience with edge/low-compute deployment
• Contributions to open-source or research publications in computer vision/generative AI
• Prior experience scaling ML systems for millions of real-world users
• Experience leading a small ML pod.

What We Offer

• The opportunity to own the ML architecture of a cutting-edge consumer health AI product
• Access to modern compute and datasets to experiment, train, and deploy
• Medical, Dental, Vision, and 401k through Justworks
• Stock options
• Parental leave (12 weeks maternity, 4 weeks paternity)
• Annual performance reviews
• Professional development budget (conferences, courses, publications)
• Flexible working arrangements, collaborative culture, and a mission-driven environment

RepeatMD is an Equal Opportunity Employer. We highly value diversity of thought and experience at our company and encourage people of all backgrounds, experiences, abilities and perspectives to apply. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, disability status, or other protected characteristics.

RepeatMD is an Equal Opportunity Employer. We highly value diversity of thought and experience at our company and encourage people of all backgrounds, experiences, abilities and perspectives to apply. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, disability status, or other protected characteristics.