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About This Role
WHAT MAKES US A GREAT PLACE TO WORK
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We are proud to be consistently recognized as one of the world’s best places to work. We are currently the top ranked consulting firm on Glassdoor’s Best Places to Work list and have earned the \#1 overall spot a record seven times. Extraordinary teams are at the heart of our business strategy, but these don’t happen by chance. They require intentional focus on bringing together a broad set of backgrounds, cultures, experiences, perspectives, and skills in a supportive and inclusive work environment. We hire people with exceptional talent and create an environment in which every individual can thrive professionally and personally.
WHO YOU’LL WORK WITH
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You’ll join our Enterprise Technology organization, partnering closely with engineering, product, and data teams to build the next generation of AI\- and machine learning\-powered solutions across Bain. Working in a highly collaborative environment, you’ll help define the technical direction of ML platforms that enable scalable, reliable, and impactful solutions for internal users and client\-facing products.
WHERE YOU’LL FIT WITHIN THE TEAM
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As a Staff Engineer, Machine Learning, you’ll play a critical role in defining the architecture, engineering standards, and operational excellence of Bain’s machine learning ecosystem. You'll partner with cross\-functional teams to build scalable ML and LLM\-powered systems, establish engineering best practices, and translate complex business challenges into robust technical solutions.
This role is ideal for someone who enjoys solving complex engineering problems, influencing technical strategy, and mentoring other engineers while remaining hands\-on with modern AI technologies.
WHAT YOU’LL DO
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### Architect \& Build ML Systems
- Design and evolve scalable machine learning pipelines supporting analytics, Q\&A, and insight generation across products
- Select and implement appropriate ML and LLM architectures based on quality, latency, scalability, and cost considerations
- Design resilient systems capable of handling evolving datasets while continuously improving model performance
### Deploy \& Operate Production ML Platforms
- Lead production deployment of LLM\-powered systems using both open\-source models and commercial APIs
- Define service level objectives (SLOs) and ensure system reliability, scalability, and operational efficiency
- Develop deployment strategies and optimize inference workloads through capacity planning
### Own ML Platforms End\-to\-End
- Lead the design, implementation, and long\-term operation of business\-critical ML systems
- Define operational KPIs and engineering standards
- Lead root cause analysis and postmortems while driving continuous improvements
- Translate complex business needs into scalable engineering solutions
### Improve Data Quality \& Model Performance
- Establish robust data contracts and monitoring practices
- Build evaluation frameworks including dashboards, regression testing, and slice analysis
- Continuously improve model quality while preventing regressions
### Advance MLOps \& Engineering Excellence
- Define lifecycle management for data, models, and prompts
- Establish CI/CD standards and deployment best practices for ML systems
- Improve observability, governance, and production monitoring
- Mentor engineers and elevate technical standards across teams
ABOUT YOU
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### Required Qualifications
- Bachelor's degree in Computer Science, Engineering, Data Science, or a related field, or equivalent practical experience
- 6\+ years of experience in software engineering, machine learning engineering, or related technical roles
- Experience designing and operating production\-grade ML systems at scale
- Experience deploying machine learning or LLM\-powered applications into production environments
- Strong proficiency in Python, SQL, and production software development
- Experience designing scalable ML pipelines, inference systems, and retrieval architectures
- Experience implementing CI/CD practices and MLOps frameworks
- Deep understanding of NLP, transformer architectures, retrieval systems, fine\-tuning, and structured extraction
- Experience building cloud\-based ML infrastructure
- Strong analytical, communication, and problem\-solving skills
- Proven ability to mentor engineers and influence technical direction
- Advanced English proficiency (written and spoken)
### Preferred Qualifications
- Advanced degree in Computer Science, Machine Learning, or a related technical discipline
- Experience owning machine learning platforms supporting multiple teams or products
- Experience operating LLM\-powered systems with measurable business impact
- Experience establishing engineering standards across ML organizations
- Experience designing large\-scale entity resolution or data integration systems
WORKING MODEL
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This role follows a hybrid model, requiring in\-office presence at least one day per week at our Chicago office.
U.S. COMPENSATION INFORMATION
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Compensation for this role includes base salary, annual discretionary performance bonus, 401(k) plan with an annual employer contribution based on years of service and Bain’s best in class benefits package.
Some local governments in the United States require a good\-faith, reasonable salary range to be included in job postings for open roles. The estimated annualized compensation for this role is as follows:
- In Chicago, IL, the good\-faith, reasonable annualized full\-time salary range for this role is between 141k and 169k; placement within this range will vary based on several factors including, but not limited to experience, education, licensure/certifications, training, and skill level.
- Annual discretionary performance bonus
- This role may also be eligible for other elements of discretionary compensation
- 4\.5% 401(k) company contribution, which increases after 3 years of service and is 100% vested upon start date
Bain \& Company's comprehensive benefits and wellness program is designed to help employees achieve personal independence, protection, and stability in the areas most important to them and their families.
- Bain pays 100% individual employee premiums for medical, dental, and vision programs, offering one of the most comprehensive medical plans for employees without impacting your paycheck
- Generous paid time off, including parental leave, sick leave, and paid holidays
- Fully vested 401(k) company contribution
- Paid Life and Long\-Term Disability insurance
- Annual fitness reimbursements
Salary Context
This $141K-$169K range is below 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 Bain & Company, 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. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($155K) sits 29% below the category median. Disclosed range: $141K to $169K.
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.
Bain & Company AI Hiring
Bain & Company has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Chicago, IL, US. Compensation range: $169K - $169K.
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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