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About This Role
About Medworks Surgical
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Medworks Surgical is a growing, multi\-site provider of surgical support services. As we continue to scale, we're investing in the systems, automation, and technology that help our teams work smarter and operate more efficiently.
We're seeking a Business Systems \& Automation/AI Manager to lead this effort. This is a highly visible, hands\-on role that will shape our technology roadmap, optimize business systems, drive automation initiatives, and help integrate practical AI solutions across the organization.
Reporting directly to the Chief Operating Officer, you'll play a critical role in transforming manual processes into scalable systems that support sustainable growth.
What You'll Do
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### Own the Technology \& Automation Roadmap
- Lead the planning, prioritization, and execution of technology and automation initiatives.
- Manage projects from concept through implementation while keeping stakeholders aligned and informed.
### Configure \& Optimize Salesforce
- Serve as the primary Salesforce administrator and system owner.
- Build and maintain objects, workflows, validation rules, reports, dashboards, and process automation.
- Ensure Salesforce serves as the central operational hub for the business.
### Connect Business Systems
- Integrate Salesforce with Microsoft 365, financial systems, and operational tools.
- Streamline workflows and eliminate manual data entry across departments.
- Create an efficient end\-to\-end process from case capture through billing.
### Deliver Actionable Business Intelligence
- Develop and maintain dashboards and reporting solutions using Power BI or comparable platforms.
- Provide leadership with real\-time visibility into operational and financial performance.
### Drive Automation \& AI Adoption
- Identify, implement, and govern practical automation and AI solutions.
- Support tools such as Microsoft Copilot, Salesforce Agentforce, Einstein, and related technologies.
- Establish responsible usage standards and best practices across the organization.
### Manage External Resources
- Work with consultants, vendors, and technical partners to deliver projects successfully.
- Define requirements, monitor performance, and manage budgets and timelines.
### Support Data Governance \& Privacy
- Maintain appropriate data governance and privacy practices.
- Help ensure responsible management of sensitive and healthcare\-related information.
### Partner Across the Organization
- Collaborate with Operations, Finance, Sales, and Leadership teams to improve processes.
- Support user training, adoption, documentation, and ongoing system enhancements.
What You'll Bring
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- 5\+ years of experience in business systems, revenue operations, business operations, sales operations, or a similar systems\-focused role.
- Hands\-on Salesforce administration experience, including configuration and workflow automation.
- Experience integrating business systems and building dashboards and reporting solutions.
- Strong understanding of process improvement and systems design.
- Experience managing third\-party vendors and technical resources.
- Familiarity with data governance, privacy practices, and information security fundamentals.
- Excellent communication, collaboration, and stakeholder management skills.
- Ability to translate business needs into scalable technology solutions.
Preferred Qualifications
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- Salesforce Administrator and/or Platform App Builder certification.
- Experience with Microsoft 365, Power Platform, Power Automate, or Microsoft Copilot.
- Experience with Salesforce Agentforce, Einstein, or other AI\-enabled business tools.
- Background in healthcare, surgical services, field services, or multi\-site operations.
- Experience supporting a rapidly growing or private\-equity\-backed organization.
What Success Looks Like
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During your first year, you will:
- Implement core quoting and case\-to\-cash workflows within Salesforce.
- Connect critical business systems and eliminate unnecessary manual data entry.
- Replace manual reporting processes with live dashboards and real\-time insights.
- Establish a strong foundation for automation and AI adoption across the company.
- Enable Medworks Surgical to scale efficiently without increasing administrative complexity.
Why Join Medworks Surgical?
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This is an opportunity to have a direct impact on the future of a growing healthcare services organization. You'll work closely with executive leadership, influence company\-wide processes, and help build the systems, automation, and AI capabilities that support our next stage of growth.
Benefits
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Medworks Surgical offers a competitive compensation package and comprehensive benefits, including:
- Starting salary range of $110,000 \- $130,000
- Medical, dental, and vision insurance
- Paid time off and company holidays
- 401(k) plan
- Professional development opportunities
- Technology and tools to support success
Equal Opportunity Employer
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Medworks Surgical is an Equal Opportunity Employer. We are committed to creating an inclusive workplace and consider all qualified applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, veteran status, or any other legally protected characteristic.
Salary Context
This $110K-$130K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 2162 roles with salary data).
View full AI/ML Engineer salary data →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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Medworks Surgical, 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 Required
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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($120K) sits 44% below the category median. Disclosed range: $110K to $130K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Medworks Surgical AI Hiring
Medworks Surgical has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Richmond, VA, US. Compensation range: $130K - $130K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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