Interested in this AI/ML Engineer role at Medisolv Inc?
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Who We Are
Healthcare quality is complex. Medisolv helps make it manageable and actionable.
We partner with hospitals, health systems, ACOs, and payers to bring clarity to quality data, connecting clinical and claims information into a single, reliable view. More than 1,800 organizations rely on Medisolv to measure, report, and improve performance across 500\+ measures tied to CMS, The Joint Commission, and other programs for 130M\+ patient records.
With support from Bessemer Venture Partners Forge, we’re continuing to grow our platform and expand our impact by investing in advanced analytics, AI, and new capabilities that help healthcare organizations stay ahead of change and improve outcomes with confidence.
The Role
We are looking for an AI Engineer to help build and evolve the core application platform behind our clinical AI product. This is not a typical CRUD app role, and it is not purely a data engineering role either. You will work on the application logic, orchestration, operational tooling, and system integrations that allow our platform to process clinical documents, run graph\-based AI workflows, and produce reliable outputs across multiple health systems.
You will work on software that has to reconcile messy real\-world inputs, support multiple customers with different requirements, and make AI behavior operationally trustworthy. The work spans architecture, product behavior, cloud systems, LLM workflows, and internal tooling. It is highly practical, technically challenging, and directly connected to real clinical use cases.
This role is ideal for an engineer who likes owning real product behavior end to end: application architecture, workflow orchestration, reliability, developer ergonomics, cloud integrations, and the practical realities of shipping AI\-powered software in healthcare.
What You'll Be Doing:
- Core application workflows: Build and maintain the runtime workflows that retrieve clinical data, execute graph\-based reasoning pipelines, and write outputs back to the platform in a reliable, observable way.
- LLM\-powered product logic: Improve how the application uses LLMs for document understanding, evidence generation, and structured extraction, with attention to correctness, latency, cost, and traceability.
- Configuration\-driven multi\-tenant architecture: Extend the platform so it can support new facilities, clinical registries, and customer\-specific behavior through shared abstractions and designed configuration layers rather than ad hoc branching.
- Operational tooling and internal UX: Improve the tooling that supports configuration updates, node evaluation, issue\-driven workflows, and other internal product operations so that engineers and clinical teammates can work faster and safer.
- Testing and reliability: Maintain and improve the validation, testing, and monitoring layers so changes to prompts, graph logic, or facility\-specific configuration do not silently degrade output quality.
- Developer experience and maintainability: Help shape the codebase, patterns, and abstractions so the system stays understandable as we add new registries, workflows, and product surfaces.
What You’ll Accomplish \- Your Performance Objectives
In your first 30 days, you will:
- You will onboard and get to know the people, products and departments that make Medisolv run.
- Complete onboarding across engineering, product, clinical, and data teams; build working relationships with key partners and understand how the Clinical AI platform supports customer outcomes.
- Get familiar with the platform architecture, core services, data flows, deployment model, and operational tooling used to run AI workflows in production.
- Set up the local development environment, access required systems, and push meaningful code changes to become productive in the codebase.
- Learn the clinical document processing and graph\-based workflow lifecycle, including how inputs are retrieved, transformed, evaluated, and written back to the platform.
- Review current reliability, testing, observability, and prompt or workflow validation practices to understand how quality is maintained across customer\-specific configurations.
In your first 6 months, you will:
- Own an important area of the Clinical AI platform end to end, from design and implementation through operational readiness and ongoing improvement.
- Design and ship enhancements that improve multi\-tenant scalability, maintainability, and configurability across customers, facilities, and clinical registries.
- Improve internal tooling and operator workflows so engineering and clinical teammates can evaluate nodes, troubleshoot issues, and manage configuration changes more efficiently.
- Lead root\-cause analysis and reliability improvements for production issues, using observability data and test results to harden the system.
- Help raise engineering quality by contributing reusable patterns, cleaner abstractions, and better developer ergonomics across the codebase.
In your first 12 months, you will:
- Be a trusted technical owner for a significant portion of the Clinical AI platform, consistently delivering reliable, maintainable, and high\-impact product behavior.
- Drive architectural improvements that make the platform more scalable, observable, and resilient as new AI workflows, customers, and registries are added.
- Demonstrate measurable impact on product quality and operational trustworthiness through stronger validation, reduced incidents, improved workflow performance, or better model\-driven outputs.
- Influence cross\-functional roadmap decisions by bringing sound engineering judgment to product, clinical, and platform tradeoffs.
- Help shape how the team builds AI\-powered software in healthcare by contributing best practices for testing, evaluation, reliability, and responsible production operations.
Who We’re Looking For \- The Competencies That Matter
You are an owner. You take initiative, move work forward, and are comfortable owning problems from discovery through implementation. You have a strong bias for action and know how to make progress in ambiguous situations without losing sight of quality, reliability, or the needs of the end user.
Systems thinker and builder. You can break down complex technical problems, design practical solutions, and build with maintainability in mind. You understand how application logic, workflows, infrastructure, and internal tooling work together, and you make thoughtful design choices that support scale and long\-term flexibility.
Collaborative and pragmatic. You work effectively across engineering, product, and clinical teams, balancing technical rigor with practical execution. You communicate clearly, incorporate feedback, and build strong working relationships that help move complex work forward.
Analytical problem solver. You know how to diagnose issues methodically, evaluate tradeoffs, and make sound decisions in complex systems. Whether the challenge is workflow behavior, model output quality, observability, or production reliability, you can identify root causes and turn insights into practical improvements.
Continuous improvement mindset. You look for better ways to build, test, validate, and operate software. You care about reducing friction for both users and internal teams, and you use automation, tooling, and process improvements to make the platform more reliable, scalable, and easier to maintain over time.
How to be a Medisolver – Our Values
- Customer Success Obsession
- All\-Star Team Collaboration
- Continuous Improvement through Curiosity \& Data\-Driven Learning
- Courage with Kindness
- Execution Focus. We Do Business, Not Just Talk Business
*Medisolv is committed to creating a diverse and inclusive workplace. We believe that diversity drives innovation, and we are dedicated to fostering an environment where all employees feel valued and respected.*
*All candidates must successfully pass a pre\-employment background check and be legally authorized to work in the United States. Sponsorship is not available.*
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Medisolv Inc, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills in Demand for This Role
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Medisolv Inc AI Hiring
Medisolv Inc has 2 open AI roles right now. They're hiring across AI Product Manager, AI/ML Engineer. Positions span Clarksville, MD, US, US.
Location Context
AI roles in Austin pay a median of $215,300 across 523 tracked positions. That's 8% above the national median.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (112) are outnumbered by mid-level (1,798) and senior (1,516) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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