SmithRx is building ML into the core of how pharmacy benefits run: detecting anomalies, optimizing claims, validating eligibility, and layering in GenAI where it actually improves decisions. If you like shipping models that end up inside real workflows (not trapped in notebooks), this is that lane.
About SmithRx
SmithRx is a venture-backed health tech company reshaping Pharmacy Benefit Management with a modern platform built for real-time insights, cost savings, and better experiences. They’re mission-driven, growing fast, and operating in a regulated healthcare environment where accuracy and trust matter.
Schedule
- Full-time, remote
- Cross-functional work with Platform Engineering, AI, Product, Data Engineering, and SMEs
- Mix of customer-facing and internal ML tooling
- Expectations include production-grade delivery (model monitoring, iteration, and scalability)
What You’ll Do
- Design and implement end-to-end ML solutions across high-impact domains like anomaly detection, claims optimization, and eligibility validation
- Productionize models with software and data engineers and integrate them into business workflows
- Mine claims data for new ML use cases that improve decisions, operations, and outcomes
- Identify and prototype GenAI use cases that increase automation, transparency, and customer experience
- Help build scalable ML infrastructure and establish best practices for model development, evaluation, and monitoring
- Serve as a thought partner on emerging AI capabilities (especially GenAI) and evaluate fit for PBM problems
What You Need
- 5+ years in data science / machine learning / AI with a track record of leading impactful initiatives
- MS or PhD in CS, EE, Statistics, Robotics, or related field (or equivalent)
- Strong applied ML foundations: regression/classification, supervised + unsupervised learning
- Strong math fundamentals: linear algebra, calculus, probability, statistics
- Python proficiency and solid object-oriented programming
- Practical MLOps experience: deploying models, monitoring, and iterating with incremental improvements
- Experience across a broad range of ML techniques and tools
- Strong retrieval/embedding experience (RAG-style approaches, similarity search, etc.)
- Hands-on with common Python ML/DL stack: pandas, scikit-learn, TensorFlow, PyTorch, transformers, LangChain (and similar)
- Ability to drive projects independently and collaboratively across teams
- Strong communication skills, including explaining complex concepts to non-technical stakeholders
Benefits
- Medical, pharmacy, dental, vision
- Life and AD&D insurance
- Flexible Spending Accounts (FSA)
- 401(k) retirement program
- Short-term and long-term disability
- Discretionary PTO
- 12 paid company holidays
- Wellness benefits
- Commuter benefits
- Paid parental leave
- Employee Assistance Program (EAP)
- Professional development and training
- Well-stocked kitchens in office locations
Apply soon — roles like this go fast when the scope is “ML + GenAI + production + healthcare.”
If you want my blunt take: the “Senior ML Engineer” title is accurate, but the job reads like “ML tech lead who can ship.” If someone only wants to model and not productionize, they’ll struggle here.
Happy Hunting,
~Two Chicks…