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WHAT MAKES US A GREAT PLACE TO WORK
We are proud to be consistently recognized as one of the world's best places to work, a champion of diversity and a model of social responsibility. We are currently ranked the number one consulting firm on Glassdoor's Best Places to Work list and have maintained a spot in the top four since its founding in 2009. Extraordinary teams are at the heart of our business strategy, but these do not 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
You will be part of a team building Bain's integrated suite of agentic decisioning tools that gives large organizations always-on intelligence across their most important growth decisions. The role sits at an intersection few organizations can offer: the analytical depth and client access of a top-tier consultancy, the technical ambition of a product company, and engineering work at the frontier of applied AI.
You will work inside Bain's standing product team, building software that helps the world's largest companies answer the questions that drive their business: where to invest, where to focus, and how to win. The impact of what you build is direct and immediate, and the platform compounds in value with every release.
The platform has two layers working together: an agentic core and a set of decision modules, each solving a distinct commercial problem. The build lifecycle is connected to the real world: as case teams work alongside clients across industries and geographies, that feedback flows straight back to engineering and into the product. Every client engagement makes the platform smarter, broader, and more resilient.
This is a startup inside an established company. You get genuine early-stage ownership and real influence over architecture and product direction, backed by Bain's global scale, client relationships, and leadership commitment; the pace of a startup with the runway of a global firm.
We are looking for product managers who see how to make a product better and bring that instinct to every sprint without being asked, and who are comfortable with problems that do not have a known answer. The work involves platform architecture tradeoffs, roadmap prioritization across product modules, and data problems with no off-the-shelf answer. Experience with agentic systems matters: enough technical fluency to hold a conversation with a senior AI engineer without needing translation, and to make credible calls at the intersection of product strategy and platform architecture. This is applied AI in production, at scale, with real users depending on what you build. The field is moving quickly and the team is built to move with it, so the willingness to keep learning counts for as much as what you already know.
WHERE YOU’LL FIT WITHIN THE TEAM
The Product Manager for the Agentic Core and Interface owns the product direction for the shared platform that the entire Growth AI Performance System suite is built on. This is not a module-level PM role: you are not shipping features for one product. You are defining the roadmap for the central intelligence layer: the agentic workflows, retrieval and knowledge pipelines, evaluation infrastructure, design system, and interface components that marketing ROI, brand strategy, and pricing and commercial analytics modules all depend on. What you prioritize shapes what every product team can build.
You will work across the platform and product modules focused on marketing ROI, brand or pricing strategy, sitting at the point where platform capability meets product need. You will translate the requirements of three module teams into a coherent platform roadmap, make the prioritization calls that allow the platform to scale without fragmenting, and hold the product vision for what the Agentic Core should be capable of twelve months from now and why.
This role requires a rare combination: deep enough technical fluency to make credible architecture-adjacent product decisions alongside the Tech Lead, deep enough commercial fluency to represent the platform's capabilities to senior client stakeholders, and deep enough user empathy to understand what Expert Consultants actually need when they are in a room with a CMO making a commercial decision. You will be the person who connects all three.
The build lifecycle here is directly connected to live client deployments. Feedback from consulting teams in the field flows back to you, and you are responsible for deciding what it means for the roadmap. You will engage directly with senior client stakeholders, presenting the platform's direction, understanding their requirements, and using that to inform what gets built next. The pace is fast, the decisions are consequential, and the product compounds in value with every sprint.
WHAT YOU’LL DO
Product strategy and roadmap ownership
- Own the product roadmap for the Growth AI Performance System Agentic Core and Interface, defining the 6-12 month vision, making the prioritization decisions that balance immediate module team needs against long-term platform scalability, and holding accountability for roadmap delivery alongside the Tech Lead
- Translate the requirements of three product module teams (marketing ROI, brand strategy, and pricing and commercial analytics), into a coherent platform backlog: identifying shared needs, building reusable platform capabilities rather than module-specific solutions, and managing the prioritization tensions that arise when three teams need different things from the same platform
- Define what the Agentic Core needs to be capable of to support the next generation of client deployments: what agentic workflows need to be built, what retrieval and knowledge capabilities need to be added, what evaluation infrastructure needs to mature, and in what order: with clear rationale grounded in both client need and technical feasibility
- Make clear, defensible prioritization decisions at pace: balancing the immediate requirements of live client deployments against the longer-term platform build, and communicating those decisions and their rationale clearly to engineering, product module teams, and practice leadership
- Own the product documentation that the platform runs on: feature specifications, API requirements, interface standards, evaluation criteria, and the onboarding materials that allow new engineering team members to contribute effectively from day one
Discovery, research and client deployment
- Run structured discovery with Expert Consultants, field specialists, and senior stakeholders to understand how the Agentic Core and Interface are being used in live engagements, where they create value, where they create friction, and where they need to evolve to keep pace with the commercial problems clients are bringing to the practice
- Define and track the metrics that tell you whether the platform is working not just delivery metrics but impact metrics: whether the platform is making consulting teams faster, whether it is producing outputs clients trust, and whether it is compounding in value across engagements as it should
- Lead the deployment process for new Agentic Core capabilities: defining what readiness means, coordinating across engineering, and consulting teams to ensure deployments land well, and owning the feedback loop that turns deployment experience into product improvement
- Validate product direction through prototype testing and structured research with real users; bring the voice of the Expert Consultant and the client into every major product decision before it becomes a sprint commitment
Stakeholder management
- Engage directly with senior stakeholders to understand their commercial requirements, present the platform's direction and capabilities, and build the trust that makes clients willing to depend on the platform in high-stakes commercial decisions
- Act as the product voice in stakeholder sessions alongside the Tech Lead and consulting team leadership: presenting what the product can do today, what it will be capable of in the next release, and why the roadmap is sequenced the way it is
- Manage the stakeholder relationships that determine whether the platform gets the resources, support, and access it needs to scale: practice leadership, case team leadership, client technology and innovation leaders, and the broader Bain technology and product organization
- Communicate product direction and delivery status clearly and consistently across all stakeholders: from sprint review updates for the engineering team through to quarterly roadmap presentations for practice and firm leadership
- May take on direct management responsibilities depending on business needs, including leading recruiting efforts, making hiring decisions, and owning the onboarding of new team members
ABOUT YOU
Experience
- 10 or more years in product management roles, with significant experience on AI-powered, data platform, or developer-facing products, including:
- Direct product leadership experience leading teams, including teams delivering AI/ML-enabled features or products, and direct management experience where the role includes direct reports
- Track record of owning a platform or shared infrastructure product roadmap, making the prioritization calls that serve multiple downstream teams while maintaining a coherent long-term vision
- Direct experience in client-facing or consulting-adjacent product environments where product decisions must survive contact with real commercial problems and real senior stakeholders
Technical
- Deep technical fluency in AI and agentic systems: able to make credible product decisions at the interface of product strategy and platform architecture, understand the engineering tradeoffs that underpin roadmap choices, and hold a technically informed conversation with a senior AI engineer without needing translation
- Experience as a PM on AI-powered, developer-facing, or data platform products; strong understanding of what it means to build a shared platform that multiple product teams depend on, and the prioritization discipline that requires
- Strong data literacy: able to define the metrics that matter for an AI platform, interpret analytical outputs honestly, and use data to inform product decisions rather than to justify decisions already made
- Experience with agentic AI systems, LLM-powered applications, or advanced analytics platforms (as a PM, a builder, or a close technical collaborator), sufficient to understand what is hard, what is fast, and what is not yet possible
- Familiarity with the commercial analytics domains the platform serves (marketing ROI and media measurement, brand and consumer analytics, pricing and revenue growth management), sufficient to understand what clients are trying to do and why the platform's outputs matter to them
- Strong product craft: able to write precise, unambiguous feature specifications, define clear acceptance criteria, run structured discovery, and produce the documentation that allows a fast-moving engineering team to build without repeated clarification
- Native AI fluency: active use of AI-assisted tools across research, documentation, synthesis, and prototype development; demonstrated commitment to staying current as the field moves
- Experience with agile delivery: sprint planning, backlog grooming, retrospectives, and the discipline to make prioritization decisions at pace without losing strategic coherence
U.S. COMPENSATION INFORMATION
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 (details listed below).
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 Massachusetts, the good-faith, reasonable annualized full-time salary range for this role is between $204,750 - $245,750
- In Illinois, the good-faith, reasonable annualized full-time salary range for this role is between $196,000 - $235,250
- Annual discretionary performance bonus
- This role may also be eligible for other elements of discretionary compensation
Benefits:
- 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 you and your family.
- 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 $196K-$245K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $178K across 2239 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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 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 $212,040 based on 13,780 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,250. Disclosed range: $196K to $245K.
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.
Bain & Company AI Hiring
Bain & Company has 10 open AI roles right now. They're hiring across AI/ML Engineer, AI Engineering Manager. Positions span Chicago, IL, US, Chicago, IL, Boston, MA, US. Compensation range: $90K - $276K.
Location Context
AI roles in Chicago pay a median of $193,440 across 450 tracked positions. That's 9% 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 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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