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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
The Associate is an onsite client\-facing role responsible for leading discrete workstreams supporting process improvement, intelligent automation, and AI\-enabled transformation initiatives. Associates independently perform analysis, facilitate client working sessions, develop recommendations, and collaborate with business and technical teams to design solutions that improve operational performance.
### RESPONSIBILITIES:
Decision Making \& Influence
- Independently manage assigned workstreams and deliver high\-quality client outcomes with limited oversight.
- Prioritize work, identify risks, and recommend practical solutions to engagement leadership.
- Manage project activities, timelines, and deliverables for assigned workstreams.
Client Service Delivery
- Facilitate client interviews, workshops, and working sessions to understand business objectives and operational challenges.
- Analyze business processes, operating models, data, and technologies to identify improvement opportunities.
- Develop future\-state process designs and implementation recommendations.
- Prepare executive\-ready deliverables, business cases, roadmaps, and presentations.
- Coordinate effectively across client stakeholders, technical teams, and engagement leadership.
- Support implementation planning, organizational readiness, and adoption activities.
*Capability\-Specific Examples (IO – Automation \& AI)*
- Lead process discovery and operational assessments to identify automation and AI opportunities.
- Evaluate business processes for automation feasibility, value, complexity, and implementation readiness.
- Translate business requirements into functional specifications, user stories, and solution requirements.
- Partner with technical teams to design, configure, test, and deploy automation solutions.
- Support process mining, workflow redesign, and operational analytics activities.
- Develop automation business cases, value assessments, and implementation roadmaps.
- Help clients redesign work by integrating AI, automation, data, and process improvements into future\-state operations.
- Support governance, change management, and user adoption activities throughout implementation.
Business Development
- Support proposal development and client pursuits.
- Identify client needs and connect opportunities to Point B capabilities.
- Build trusted client relationships through exceptional service delivery.
Leadership
- Mentor Analysts by providing coaching, feedback, and day\-to\-day guidance.
- Foster collaboration across engagement teams.
- Demonstrate ownership, initiative, and accountability for client outcomes.
- Consistently demonstrate Point B values.
Intellectual Capital
- Contribute to development of methodologies, playbooks, accelerators, and thought leadership.
- Continue building expertise in AI, automation, enterprise technologies, and operational transformation.
- Share lessons learned and leading practices across the capability.
### REQUIRED QUALIFICATIONS:
- B.A. or B.S. degree required; MBA preferred.
- 2\+ years of consulting, business transformation, operations, process improvement, automation, technology implementation, or related experience.
- Ability to work on\-site as requested.
- Ability to work remotely.
- Ability to travel up to 80%.
- Ability to work non\-standard work hours as necessary.
### DESIRED QUALIFICATIONS:
- Experience supporting or leading workstreams on consulting engagements or business transformation initiatives.
- Experience documenting business requirements, process maps, operating procedures, or functional specifications.
- Working knowledge of process improvement methodologies (Lean, Six Sigma, BPM, Design Thinking, etc.).
- Exposure to AI, intelligent automation, process mining, low\-code platforms, enterprise applications (ERP, CRM), or workflow technologies.
- Familiarity with Agile, SDLC, or product delivery methodologies.
- Strong analytical and problem\-solving skills with experience translating business challenges into actionable recommendations.
- Experience analyzing operational or process data using tools such as Excel, Power BI, Tableau, SQL, or similar technologies.
- Experience collaborating with both business and technical stakeholders.
- Excellent written, verbal, and presentation skills.
- Experience working in one or more industries such as Healthcare, Life Sciences, Financial Services, Manufacturing, Consumer Products \& Retail, or similar industries.
The estimated salary range for this role is $69,000\- $138,000 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 $69K-$138K range is in the lower quartile 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 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 $218,750 based on 3,817 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $120,000. This role's midpoint ($103K) sits 53% below the category median. Disclosed range: $69K to $138K.
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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