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About This Role
Company Overview
ClearView Healthcare Partners is a premier life sciences strategy consulting firm headquartered in Boston, with offices in San Francisco, New York City, London, Zurich and Gurgaon. Serving clients in the biopharmaceutical, medical device, and diagnostic spaces, we provide world\-class strategic decision\-making support across a diverse range of business issues. Our goal is to inform actionable recommendations that allow companies to achieve their business objectives.
About the Job
Reporting to the CIO, with a dotted line to the Data \& Analytics Pillar Lead, ClearView’s AI Enablement Manager (AIEM) owns the ongoing program management of ClearView’s AI tool portfolio, including Claude, Perplexity, Kenley, and custom\-coded solutions, and drives the prompts, workflows, and adoption practices that help consulting teams get the most out of these tools.
The AIEM partners closely with ClearView leadership and AI initiative leads to organize ongoing efforts across the AI portfolio, prioritize where to invest next, and execute against that roadmap. They identify new use cases, design and refine prompts, build and document workflows, and ensure each tool delivers measurable value to billable teams.
This role also leads communication and enablement across firm leadership, consulting teams, IT, and other stakeholders to ensure alignment on AI priorities, rollout plans, and adoption. The AIEM also inherits and centralizes AI initiatives currently being built ad hoc by billable consultants, freeing them to focus on client work while ensuring these tools are productionized, supported, and adopted firmwide. The role requires strong leadership, the ability to manage competing priorities, and a working understanding of LLMs, prompt engineering, and modern AI/coding tools.
Responsibilities
*Key responsibilities include:*
- AI portfolio prioritization
+ Maintain a firmwide view of in\-flight and proposed AI initiatives across Claude, Perplexity, Kenley, and custom coding tools. Partner with the CIO, Data \& Analytics leadership, and pillar leads to prioritize, sequence, and resource the highest\-impact use cases.
- Prompt and workflow design
+ Author, test, and iterate on prompts, agents, and AI workflows that improve how consulting teams scope, research, analyze, and deliver work. Identify new use cases by sitting with billable teams and turning recurring pain points into reusable AI assets.
- Consultant handoffs
+ Inherit AI tools, prompts, and prototypes currently being built and maintained by billable consultants. Productionize, document, and support them so consulting teams can stay focused on client work.
- Project scoping
+ Lead requirements gathering, define scope, objectives, deliverables, and success metrics for AI enablement initiatives.
- Communication and stakeholder management
+ Keep the CIO, Data \& Analytics, pillar leads, consulting leadership, and IT informed of AI initiative progress, decisions, and trade\-offs. Translate technical detail for non\-technical audiences and vice versa.
- AI governance and coordination
+ Serve as the primary liaison with IT, compliance, legal, and learning \& development to ensure AI tools are secure, compliant with client confidentiality requirements, and rolled out responsibly.
- Enablement and training
+ Partner with learning \& development to design and deliver training, office hours, prompt libraries, and support materials that help consultants get fluent with the firm’s AI tools.
- Feedback and iteration
+ Gather feedback from consulting teams and firm leadership on AI tools in use, surface friction points, and iterate on prompts and workflows based on real usage.
- Adoption and impact
+ Measure usage, time saved, and quality impact of AI tools and workflows. Share results with leadership and recommend where to double down, sunset, or invest next.
Job Qualifications
*Qualifications:*
- A bachelor’s degree in computer science, information technology, engineering, life sciences, or other related field, or equivalent experience
- A minimum of 5 years of experience in technology, product, or consulting roles, including at least 2 years working hands\-on with LLMs, prompt engineering, or AI\-driven workflows
- Demonstrated experience running projects end\-to\-end (Agile or otherwise) and shipping AI or software tools into production use
Desired Skills \& Behaviors
*Preferred attributes to achieve this include:*
- Strong working knowledge of LLMs, prompt engineering, retrieval/agent patterns, and modern AI coding tools (e.g., Claude, Perplexity, Cursor, Copilot), with the ability to translate business needs into practical AI solutions.
- Demonstrated ability to lead and influence cross\-functional teams and coach colleagues without direct line authority.
- Exceptional communication and enablement skills, with the ability to tailor messages for both technical and non\-technical audiences, from senior leadership to junior consultants.
- Strong analytical and product\-thinking skills, including the ability to identify high\-leverage AI use cases, make data\-informed prioritization decisions, and assess initiative impact.
- Comfort with ambiguity and rapid change; the AI landscape shifts month to month, and the role requires continuously evaluating new tools and adjusting the firm’s approach.
- Excellent organization and prioritization skills, with the ability to manage a portfolio of concurrent AI initiatives and keep them on track.
Location
- Boston, New York or San Francisco
Salary to be confirmed – will be updated by 8/24\.
What We Value
We recognize that not every candidate will meet every qualification listed. If you’re excited about this role and believe you have relevant experience or transferable skills, we encourage you to apply. We value curiosity, a growth mindset, and a commitment to collaboration.
Equal Opportunity Employer
ClearView Healthcare Partners ("CV") is an Equal Opportunity employer. All qualified applicants will be considered for employment without regard to sex, gender identity, sexual orientation, race, color, religion, national origin, disability, protected Veteran status, age, or any other characteristic protected by applicable law.
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 ClearView Healthcare Partners, 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.
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.
ClearView Healthcare Partners AI Hiring
ClearView Healthcare Partners has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Newton, MA, US.
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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