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About This Role
Company Description The Medicus Firm (TMF), a part of M3USA, is a national healthcare recruitment firm with a mission to be the market leader which is most respected for its Performance, People, and Partnerships. One of the largest physician recruitment companies in the US, TMF focuses on providing the most efficient and effective recruiting services to hospitals and healthcare employers nationwide with unmatched sophistication, consultation, and market insight. Due to its transparent and consultative approach, The Medicus Firm is a nine-time winner of the Best of Staffing Client Satisfaction award, presented by ClearlyRated. By providing a collaborative work environment with a competitive compensation model, TMF has successfully built an accomplished team that is the recipient of multiple awards for its culture.
Due to our continued growth, we are hiring for an AI & Technology Enablement Specialist with The Medicus Firm, an M3 company.
As part of the M3 family of companies, The Medicus Firm benefits from M3’s physician reach through millions of active physicians who regularly participate in market research, continuing education, clinical research, professional enrichment, etc.
Job Description
The Medicus Firm (TMF) connects physicians and advanced practice providers with the healthcare organizations that need them. Our Operations team exists to make TMF’s recruiters faster, better informed, and easier for clients and candidates to work with — and this role sits at the center of that mission. You’ll turn AI and technology investments into everyday tools the team actually uses, while keeping the systems behind the business (CRM, ATS, sales operations) accurate and working.
What you’ll do:
- Lead discovery and evaluation calls with AI and technology vendors, documenting findings and driving go/no-go decisions
- Own integration of approved AI tools from kickoff through go-live, bridging leadership, IT, and vendors
- Design and deliver training tailored to how recruiters, sales, and marketing actually work
- Partner with team leads to build and roll out AI-supported workflows for sourcing, outreach, screening, and reporting
- Track adoption, gather feedback, refresh training, and retire tools that aren’t earning their keep
- Maintain an inventory of AI and technology tools, licenses, renewals, and owners
- Support sales leadership with pipeline reporting, CRM hygiene, and contract/proposal templates
- Identify sales ops tasks ripe for automation or AI assistance
- Define and maintain CRM/ATS data standards, run quality checks, and coach staff on data entry
Qualifications
- Bachelor’s degree in business, information systems, or related field, or equivalent experience
- 3+ years in technology enablement, solutions, implementation, customer success, revenue operations, or sales operations
- Hands-on experience implementing or supporting a CRM or ATS (Bullhorn, Salesforce, HubSpot, or similar)
- Proven track record rolling out AI tools or automation to non-technical teams
- Staffing, recruiting, physician recruitment, or healthcare technology experience a plus
- Strong facilitation skills — comfortable running a vendor call and a leadership discussion in the same day
- Ability to document workflows and write clear, plain-language guides
- Comfortable working with data: building reports, spotting quality issues, explaining what they mean
Additional Information About M3USA
M3 USA is at the forefront of healthcare innovation, offering digital solutions across healthcare, life sciences, pharmaceuticals, and more. Since our inception in 2000, we’ve seen remarkable growth, fueled by our mission to utilize the internet for a healthier world and more efficient healthcare systems.
Our success is anchored in our trusted digital platforms that engage physician communities globally, facilitating impactful medical education, precise job placement, and insightful market research. M3 USA prides itself on a dynamic and innovative work environment where every team member contributes to global health advancements.
Joining M3 USA means being part of a dedicated team striving to make a significant difference in healthcare. We provide a unique opportunity for you to be at the cutting edge of healthcare innovation, shaping the future in a meaningful career. Embrace the chance to drive change with M3 USA.
Benefits:
A career opportunity with M3USA offers competitive wages, and benefits such as:
- Health and Dental
- Life, Accident and Disability Insurance
- Prescription Plan
- Flexible Spending Account
- 401k Plan and Match
- Paid Holidays and Vacation
- Sick Days and Personal Day
- M3 reserves the right to change this job description to meet the business needs of the organization
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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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At m3usa, 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 $212,040 based on 13,780 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
m3usa AI Hiring
m3usa has 2 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer. Positions span Dallas, TX, US, Fort Washington, PA, US.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 roles, $210,000 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 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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