Interested in this AI/ML Engineer role at Point B?
Apply Now →About This Role
Point B is a business innovation firm that takes the guesswork out of transformation. We engineer your future by combining advanced technologies and industry expertise to help you reimagine your business and its processes to get ahead and stay ahead.
We're consulting done different. While others might say it, we live it—your success is our success.
We start with the challenges you face, then partner to drive to what’s right for your business, your people, and your future. The proof is in our world\-class NPS score that consistently triples our competitors. We know how to listen carefully, respond with agility, and accelerate time to value.
When you partner with Point B, you’ll experience the speed and confidence needed to spot critical pivots, navigate complexity with ease, and tailor technology to fit your needs.
We're ready to start generating your future today.
### JOB SUMMARY:
Point B is seeking a Senior Strategy Manager to help clients design and execute enterprise growth strategies spanning Product, Service, Marketing, and Sales functions. This role focuses on defining business strategies – enterprise, growth, product/service, and go\-to\-market. This role is scoped primarily to work within the healthcare Payer industry, bringing foundational understanding of Payer\-specific challenges, opportunities and business context.
You will work with senior executives to define/refine business strategies, create business cases to drive decision making, and translate those strategies into actionable transformation roadmaps across the Payer member/customer/employer group lifecycle and/or value streams. This is a business strategy and transformation role, emphasizing growth architecture, product and service strategy, and organizational capability design rather than technical implementation.
As a Strategy Senior Manager, you are responsible for developing others in this area of expertise, directly delivering alongside clients, contributing to growth of a portfolio book of business through thought leadership, solution architecture and driving new sales and relationships.
### RESPONSIBILITIES:
Strategy Development
- Shape enterprise strategy by identifying growth opportunities, setting strategic priorities, and aligning investments to deliver long\-term value.
- Guide organizations in weighing enterprise strategies to progress their vision and goals; develop business cases to drive investment decisions
- Define product and service portfolio strategies aligned to enterprise growth objectives.
- Support product\-market fit analysis, pricing and monetization strategy, and value proposition development.
- Shape service offerings and commercialization strategies that improve customer acquisition and retention and position in the marketplace and enable a sustainable growth plan for the enterprise.
- Develop integrated GTM strategies across demand generation, partner ecosystems, sales motions, and customer lifecycle management.
- Define segmentation, targeting, positioning, and messaging strategies supported by data\-driven insights.
- Translate growth strategies into executable roadmaps with measurable KPIs.
Capability Assessment \& Operating Model Design
- Evaluate enterprise capabilities and architect operating models that align people, processes, and technology to accelerate growth.
- Benchmark organizational capabilities, competitive differentiators, and define future\-state operating models.
- Identify capability gaps and develop phased transformation strategies to achieve growth strategies.
Business Development
- Collaborates with Industry and Service Line leadership to evaluate opportunities, sell, and deliver innovative results for new and existing clients.
- Develops thought leadership, client insight articles and represents Point B by presenting at client facing forums and conferences.
- Develops and maintains network at the executive level within healthcare, Payer
- Direct role in business development, supporting generation of new sales and solution architecture in the business strategy and transformation domain
Core Competencies
- Growth and go\-to\-market strategy development
- Product and service strategy
- AI\-enabled transformation and capability modeling
- Executive facilitation and storytelling
- Business development
### REQUIRED QUALIFICATIONS:
- 10\+ years of experience in strategy consulting, such as growth, product and/or GTM strategy / transformation.
- Experience delivering strategy work in healthcare and/or health insurance/Payer
- Demonstrated experience advising senior stakeholders on growth strategy, product/service strategy, or go\-to\-market transformation initiatives.
- Proven ability to conduct capability assessments, operating model design, or maturity benchmarking
- Strong business\-side understanding of:
Product and service strategy
CRM and customer lifecycle strategy
Lead management and demand generation
AI\-enabled business transformation (use\-case definition and strategic adoption)
- Exceptional executive communication, structured problem solving, and consulting storytelling skills
### DESIRED QUALIFICATIONS:
- MBA or MSc in Business, Strategy, Marketing, Management, or a related discipline.
- Experience in a high\-performing strategy consulting environment or an equivalent corporate strategy function within a Fortune 500 or scaled mid\-market organization.
- Ability to architect strategy engagements, develop a portfolio of work and cultivate executive relationships.
- Experience designing or supporting Lead\-to\-Cash (L2C), Demand\-to\-Revenue, Product\-Led Growth, or AI\-enabled GTM initiatives.
- Familiarity with enterprise transformation frameworks (e.g., CVM, ADKAR, Fit\-for\-Growth, or similar methodologies).
- Experience facilitating executive workshops and building board\-level strategy narratives.
- Trained/Certified in relevant areas of expertise, such as MarTech/CRM and/or Consulting Communication \& Product Strategy, Digital and AI Strategy, and/or Consulting/Business Agility.
The estimated salary range for this role is $118,500 \- $263,500 USD per year. This salary range is provided as required by local and state law as applicable. Individual salaries vary on a number of factors, including but not limited to geography, skills, education, experience and unique qualifications where applicable.
Bonuses are awarded at Point B’s discretion and are based upon individual contributions and overall firm performance.
Employees hired on or after October 1 are not eligible to participate in the current year’s bonus or merit increase cycle. Eligibility will begin with the following performance cycle, in accordance with company policy.
INTRIGUED TO LEARN MORE?
When you apply for this role, your information will be personally reviewed by our talent acquisition team (not by a robot). You can expect to hear back from us with feedback if we think there could be a fit and what next steps look like.
WHAT MAKES POINT B DIFFERENT?
We put our passion for change to work, using our purpose and values as our north star. Our teams help organizations solve their greatest challenges and created an inclusive culture that attracts and retains the world’s best talent. Be part of a collaborative culture where we build lasting relationships with each other, our customers, and our communities.
Benefits – Point B rewards high performance with a total rewards approach that includes competitive base pay, benefits, as well as flexibility, leadership development opportunities, and a culture designed to help our diverse team of individuals flourish.
Award winning – Point B has been consistently recognized as one of the best places to work by Fortune magazine, Great Place to Work, Consulting Magazine, BuiltIn, and many others. We are proud to be named a Best Workplace in the US by Fortune magazine, Best Workplaces for Millennials, and Best Workplaces for Women in addition to other awards regarding our workplace inclusivity.
Point B is an equal\-opportunity employer committed to a diverse workforce. We provide equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws. You can read more about our commitment to diversity on our website.
Point B is committed to providing equal opportunities for persons with disabilities or religious observances, which includes providing reasonable accommodation for in any individuals with disabilities or for religious purposes. Applicants with disabilities may contact our Accommodations team at applicantaccommodations@pointb.com or 206\-517\-5000 to request and arrange for accommodations through the application and/or recruiting process. If you need assistance to accommodate a disability or religious observance, you may request an accommodation at any time. Please note: This mailbox is only for accommodation requests or questions. Please use the Contact Us form for any recruiting inquires.
Legal Information for Job Seekers can be accessed on our Careers Website.
Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.
Salary Context
This $118K-$263K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Point B, 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($191K) sits 13% below the category median. Disclosed range: $118K to $263K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Point B AI Hiring
Point B has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Chicago, IL, US. Compensation range: $138K - $263K.
Location Context
AI roles in Chicago pay a median of $205,100 across 97 tracked positions. That's 6% below 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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