Interested in this AI/ML Engineer role at Accenture?
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We Are
Accenture is building a new portfolio of agentic software products in the industries we have spent decades helping to transform. We begin with advantages most product companies do not have: deep industry expertise, trusted relationships with many of the world’s largest organizations, and direct access to the complex workflows and problems that matter most to them. That allows us to build closely with customers while creating repeatable products that can be taken broadly to market.
We are forming small, highly capable product teams to do this. These teams bring product management, design, and engineering together with the autonomy to move quickly, make decisions, and own outcomes from the earliest prototype through production and scale.
This is a product management role within a permanent product team. It is not a consulting delivery, program management, project management, product owner, or forward\-deployed role.
You Are
You are an exceptional AI\-native product manager who has taken meaningful products or product areas from zero to one and owned them through launch, adoption, and growth. You combine strong product judgment with customer empathy, commercial instinct, analytical rigor, and a high bar for quality. You are comfortable starting with an ambiguous customer problem, developing a clear point of view, and working with design and engineering to turn it into a product that customers value and use.
You do not treat AI as a feature to add to an otherwise conventional product. You understand how agentic systems change what a product can do, how users interact with it, how work is completed, and where human judgment should remain in the loop.
You are also AI\-native in how you work. You use AI tools extensively to accelerate research, synthesize customer input, explore product concepts, test assumptions, draft specifications, analyze data, and improve the quality and speed of your decisions.
You are energized by small teams, high autonomy, direct customer exposure, and the opportunity to help define how a new product organization should operate.
What You Will Do:
Discover important customer problems. Develop a deep understanding of the industries, users, workflows, and business problems your product is intended to address. Work directly with customers, design partners, domain experts, sales teams, and industry leaders to identify where a product can create meaningful and repeatable value. Separate genuine market pull from one\-off customer requests, internal enthusiasm, or services opportunities.
Define the product direction. Set a clear product vision, strategy, and near\-term thesis for your area. Define what the product will do, who it is for, why it will win, and what the team should deliberately not build. Turn ambiguous opportunities into focused product bets that a small team can test and ship quickly. Make clear tradeoffs across customer value, technical feasibility, commercial potential, speed, quality, and strategic fit.
Build AI\-native products. Shape products around the capabilities and limitations of modern AI systems rather than applying AI superficially to existing workflows. Define how agents should reason, act, use tools, interact with users, handle uncertainty, and escalate when human judgment is required. Work with engineering and design to define product behavior, evaluations, quality thresholds, failure handling, trust mechanisms, and human\-in\-the\-loop experiences.
Lead zero\-to\-one product development. Work as part of a small, cross\-functional team with design and engineering from the earliest stages of discovery. Move quickly from customer problem to prototype, design\-partner validation, production product, and broader market launch. Write clear product briefs, requirements, decision documents, and acceptance criteria without creating unnecessary process or overhead. Use prototypes and working software to learn rather than relying primarily on presentations, lengthy planning cycles, or abstract market research.
Own product quality. Hold a high bar for the end\-to\-end customer experience, not simply feature completion. Work closely with design to ensure the product is intuitive, coherent, and appropriate for the complexity of the workflow. Define what good looks like through clear product outcomes, behavioral expectations, evaluation criteria, and quality thresholds. Treat the quality of AI outputs, user trust, and operational reliability as core product responsibilities.
Validate demand and commercial value. Build with design partners and early customers to test whether the product solves an important enough problem to earn sustained usage and budget. Define the hypotheses that must be proven across desirability, usability, technical performance, business value, and willingness to pay. Partner with commercial and industry leaders on positioning, packaging, pricing, routes to market, and launch strategy. Distinguish between customer interest, pilot activity, product adoption, and durable market demand.
Own outcomes after launch. Define the product’s goals, success measures, and key results. Use product data, customer behavior, qualitative feedback, support signals, evaluations, and commercial performance to understand what is working and what is not. Own adoption, usage, retention, customer value, and product quality—not simply roadmap delivery.
Operate AI\-native. Use AI tools as a core part of your daily product management workflow. Apply AI to customer research, synthesis, competitive analysis, product exploration, prototyping, data analysis, documentation, and decision support. Build repeatable context, prompts, workflows, and tools that improve your own leverage and the effectiveness of the broader product team. Understand the limits of AI\-generated analysis and remain accountable for the quality of the decisions that result.
Raise the product bar. Bring strong product judgment and help the team distinguish between adequate and exceptional work. Create clarity when the team faces ambiguity, competing priorities, or incomplete information. Help other product managers improve their customer thinking, prioritization, product judgment, and AI\-native practices. Contribute to the shared principles, standards, tools, and operating model of the broader product organization.
Working Model
This is a hybrid role based in Seattle, Mountain View, San Francisco, San Jose, or New York. Team members are expected to work regularly with their local product team in person. The role may involve periodic travel to meet with customers, design partners, or other product teams. It is not a client\-staffing or full\-time travel role.
Here's What You Need:
- Minimum 8 years of experience in product management, including ownership and delivery of enterprise software products, digital platforms, or technology\-enabled business solutions.
- Minimum 8 years of experience leading products, major capabilities, platforms, or new business initiatives from concept through launch, adoption, and ongoing optimization.
- Minimum 2 years of substantive, hands\-on experience building, launching, or managing AI\-native products as part of day\-to\-day product management responsibilities.
- Minimum 2 years of experience defining and delivering products that leverage large language models (LLMs), AI agents, retrieval systems, tool integrations, workflow automation, or other generative AI technologies.
- Minimum 2 years of experience evaluating and managing AI product performance, including model capabilities, limitations, reliability, cost considerations, risk mitigation, and operational scalability.
- Minimum 8 years of experience partnering closely with software engineers, UX designers, and cross\-functional stakeholders to deliver high\-quality production software.
- Minimum 8 years of experience conducting customer discovery, user research, stakeholder interviews, or market analysis to identify and validate product opportunities.
- Minimum 8 years of experience simplifying complex business processes, workflows, or user experiences into scalable product solutions.
- Minimum 8 years of experience defining product strategy, establishing product roadmaps, prioritizing investments, and making tradeoff decisions in evolving or ambiguous environments.
- Minimum 8 years of experience using quantitative and qualitative data, including customer feedback, product analytics, experimentation, and market insights, to inform product decisions.
- Minimum 8 years of experience contributing to commercial product outcomes, including customer adoption, value realization, pricing strategy, revenue growth, market expansion, or business case development.
- Minimum 8 years of experience presenting product strategy, business cases, roadmap decisions, and product performance metrics to executive leaders, customers, and cross\-functional stakeholders.
- Minimum 2 years of experience utilizing AI\-powered productivity tools, copilots, analytics platforms, or automation solutions to improve product management effectiveness and decision\-making.
- Minimum 8 years of experience working independently with significant ownership and accountability in fast\-paced, ambiguous, or high\-growth environments.
- Bachelor's degree or equivalent (minimum 12 years) work experience. If Associate's degree, must have a minimum of 6 years of work experience.
Compensation at Accenture varies depending on a wide array of factors, which may include but are not limited to the specific office location, role, skill set, and level of experience. As required by local law, Accenture provides a reasonable range of compensation for roles that may be hired as set forth below.
We anticipate this job posting will be posted until 09/13/2026\.
Accenture offers a market competitive suite of benefits including medical, dental, vision, life, and long\-term disability coverage, a 401(k) plan, bonus opportunities, paid holidays, and paid time off. See more information on our benefits here:
U.S. Employee Benefits \| Accenture
Role Location Annual Salary Range
California $163,000 to $434,000
Cleveland $150,900 to $347,200
Colorado $163,000 to $375,000
District of Columbia $173,500 to $399,300
Illinois $150,900 to $375,000
Maine $138,800 to $319,400
Maryland $163,000 to $375,000
Massachusetts $163,000 to $399,300
Minnesota $163,000 to $375,000
New York $150,900 to $434,000
New Jersey $173,500 to $434,000
Virginia $150,900 to $399,300
Washington $173,500 to $399,300
Requesting an Accommodation
Accenture is committed to providing equal employment opportunities for persons with disabilities or religious observances, including reasonable accommodation when needed. If you are hired by Accenture and require accommodation to perform the essential functions of your role, you will be asked to participate in our reasonable accommodation process. Accommodations made to facilitate the recruiting process are not a guarantee of future or continued accommodations once hired.
If you would like to be considered for employment opportunities with Accenture and have accommodation needs such as for a disability or religious observance, please call us toll free at 1 (877\) 889\-9009 or send us an email or speak with your recruiter.
Equal Employment Opportunity Statement
We believe that no one should be discriminated against because of their differences. All employment decisions shall be made without regard to age, race, creed, color, religion, sex, national origin, ancestry, disability status, veteran status, sexual orientation, gender identity or expression, genetic information, marital status, citizenship status or any other basis as protected by federal, state, or local law. Our rich diversity makes us more innovative, more competitive, and more creative, which helps us better serve our clients and our communities.
For details, view a copy of the Accenture Equal Opportunity Statement
Accenture is an EEO and Affirmative Action Employer of Veterans/Individuals with Disabilities.
Accenture is committed to providing veteran employment opportunities to our service men and women.
Other Employment Statements
Applicants for employment in the US must have work authorization that does not now or in the future require sponsorship of a visa for employment authorization in the United States.
Candidates who are currently employed by a client of Accenture or an affiliated Accenture business may not be eligible for consideration.
Job candidates will not be obligated to disclose sealed or expunged records of conviction or arrest as part of the hiring process. Further, at Accenture a criminal conviction history is not an absolute bar to employment.
The Company will not discharge or in any other manner discriminate against employees or applicants because they have inquired about, discussed, or disclosed their own pay or the pay of another employee or applicant. Additionally, employees who have access to the compensation information of other employees or applicants as a part of their essential job functions cannot disclose the pay of other employees or applicants to individuals who do not otherwise have access to compensation information, unless the disclosure is (a) in response to a formal complaint or charge, (b) in furtherance of an investigation, proceeding, hearing, or action, including an investigation conducted by the employer, or (c) consistent with the Company's legal duty to furnish information.
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Salary Context
This $150K-$434K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Accenture, 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($292K) sits 36% above the category median. Disclosed range: $150K to $434K.
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.
Accenture AI Hiring
Accenture has 23 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist, AI Software Engineer. Positions span Morristown, NJ, US, Seattle, WA, US, New York, NY, US. Compensation range: $196K - $434K.
Location Context
AI roles in Seattle pay a median of $228,700 across 516 tracked positions. That's 6% above 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 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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