Interested in this AI/ML Engineer role at Accenture?
Apply Now →About This Role
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 design role within a permanent product team. It is not a consulting design, service design, design strategy, research\-only, or forward\-deployed role.
You Are:
You are an exceptional AI\-native product designer who has taken complex software products or major product areas from zero to one and owned the experience through launch, adoption, and continuous improvement. You combine strong interaction design, systems thinking, visual craft, customer empathy, and product judgment with a high bar for quality.
You are comfortable starting with an ambiguous customer problem, developing a clear point of view, and working with product and engineering to turn it into a product that is useful, intuitive, trustworthy, and well crafted.
You understand that AI\-native products require more than placing a conversational interface on top of an existing workflow. You know how to design systems that reason, act, use tools, manage uncertainty, explain themselves, recover from failure, and involve people at the right moments.
You are also AI\-native in how you work. You use AI tools to accelerate research, synthesis, concept development, prototyping, content design, design exploration, and critique while maintaining ownership of the quality and coherence of the final experience.
You are energized by small teams, high autonomy, direct customer exposure, and the opportunity to help define how a new product design organization should operate.
What You Will Do:
Shape the product from the beginning. Work with product managers and engineers from the earliest stages of discovery to define the customer problem, product thesis, and experience strategy. Help the team decide what to build, not simply how it should look after the decision has been made. Turn ambiguity into clear concepts, flows, prototypes, and product direction.
Design AI\-native experiences. Define how users interact with agents, models, tools, and automated workflows. Design for intent, delegation, oversight, uncertainty, transparency, correction, escalation, and control. Create experiences that help users understand what the system is doing, when they should trust it, and when they need to intervene.
Simplify complex workflows. Develop a deep understanding of users, industry processes, data, constraints, and business goals. Reduce complicated enterprise workflows into experiences that feel coherent, intuitive, and purposeful without removing the controls expert users need.
Prototype to learn. Create prototypes early and often to explore product ideas, test interaction models, and learn directly from customers and design partners. Use working prototypes rather than static presentations wherever possible. Move fluidly from rough concepts to detailed production\-ready designs.
Own the end\-to\-end experience. Design across the full customer journey, including onboarding, core workflows, agent interactions, configuration, collaboration, approvals, exceptions, errors, and ongoing use. Ensure the product works as a coherent system rather than a collection of individual screens.
Set the quality bar. Hold a high standard for interaction design, visual design, content, information architecture, accessibility, and product coherence. Review the product continuously and identify weak flows, unclear language, poor defaults, inconsistent behavior, unnecessary complexity, and low\-quality details.
Work closely with engineering. Partner directly with engineers throughout implementation, not only at handoff. Understand technical constraints and possibilities, make thoughtful tradeoffs, and stay engaged through production. Use coded prototypes and AI\-assisted development tools where they improve the speed or fidelity of the work.
Learn from customers and product behavior. Work directly with customers, design partners, domain experts, and internal teams to understand needs and observe how the product is used. Combine qualitative research, product data, evaluation results, and support signals to improve the experience continuously.
Operate AI\-native. Use AI tools as a core part of your daily design workflow. Apply them to research synthesis, concept generation, prototyping, content exploration, design systems, critique, and documentation. Build reusable workflows and context that increase your own leverage and the effectiveness of the broader product team.
Raise the design bar. Help the team distinguish between functional design and exceptional product design. Provide clear, constructive critique and help other designers strengthen their product judgment, craft, and AI\-native practices. Contribute to the shared design principles, systems, tools, and operating model of the broader product organization.
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 designing, delivering, and optimizing software products, digital platforms, or user experiences in production environments.
- Minimum 8 years of experience leading the design of complex products, major product capabilities, or new business offerings from concept through launch, adoption, and ongoing enhancement.
- Minimum 2 years of substantive, hands\-on experience designing AI\-native products, intelligent workflows, or user experiences that incorporate generative AI into day\-to\-day product usage.
- Minimum 2 years of experience designing products that leverage Large Language Models (LLMs), AI agents, retrieval systems, tool integrations, workflow automation, or other advanced AI technologies.
- Minimum 2 years of experience designing for AI\-enabled user experiences, including considerations related to latency, model quality, explainability, uncertainty, reliability, human oversight, and failure recovery.
- Minimum 8 years of experience applying interaction design, information architecture, user\-centered design, prototyping, visual design, and systems thinking to enterprise or consumer software products.
- Minimum 8 years of experience designing complex enterprise, technical, or expert\-user workflows while balancing usability, efficiency, and user control.
- Minimum 8 years of experience making product and design decisions that improve usability, adoption, customer satisfaction, and business outcomes.
- Minimum 8 years of experience collaborating with product managers, software engineers, and cross\-functional stakeholders throughout the product lifecycle, from discovery through production release.
- Minimum 8 years of experience conducting customer research, user interviews, usability testing, journey mapping, or other user\-centered research methodologies and translating insights into product decisions.
- Minimum 2 years of experience utilizing AI\-powered design, research, content creation, or prototyping tools to improve design quality, productivity, and decision\-making.
- Demonstrated portfolio containing a minimum of 3 years of shipped product work that showcases end\-to\-end ownership, interaction design expertise, product thinking, and measurable user impact.
- Minimum 8 years of experience operating with significant autonomy, managing competing priorities, and making decisions in ambiguous, fast\-paced, or evolving environments.
- Minimum 8 years of experience presenting design recommendations, product concepts, user insights, and strategic decisions to engineers, product leaders, executives, customers, and domain experts.
- 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 New York pay a median of $220,000 across 1,650 tracked positions.
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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