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About This Role
AI/ML Computational Science Manager \| Senior Level \| Full time
Job No. R00345956 \| Multiple Locations
We Are
In the last 30 years, no technology has promised to change everything across a business—until generative AI. Today, AI is the number one driver of business reinvention. And data readiness is one of the most important factors for AI success. Accenture is stepping boldly into this future with a clear strategy and purpose: to help clients optimize and reinvent their business with data \& AI. With over 77,000 professionals dedicated to Data \& AI, Accenture’s Data \& AI organization is powered by experienced innovation, strategic investment, exceptional talent, and our power ecosystem.
There is never a typical day at Accenture, but that’s why we love it here! This is an extraordinary chance to create a rewarding career at Accenture Technology. Working in a digitally driven, innovation\-led environment where you can help clients shift to the New using leading\-edge technologies on some of the coolest projects you can imagine.
Join our Global Agentic Center of Excellence and help lead that change.
You Are:
As a Snowflake Advanced AI Solution Lead, you will design, build, and operationalize artificial intelligence and machine learning solutions for enterprise clients, combining custom models with cloud and third\-party AI services to deliver production\-ready outcomes. Your role spans the full solution lifecycle — assessing client needs and data, selecting and customizing models (including Deep Learning, Generative AI, and Large Language Models), designing scalable data and MLOps/LLMOps pipelines for training and production, and ensuring quality, value, and reliability of deployed systems.
The Work:
- Formulate real\-world problems into practical, efficient, and scalable AI and Machine Learning solutions
- Develop and implement machine learning algorithms, models, and computational systems; design and build scalable data pipelines to support model training and production with DevOps \& MLOps
- Customize and apply Deep Learning and Gen AI models for various use cases based on the business needs, data availability, system and infrastructure requirements \- including edge device and HPC
- Engage in research and development of new AI and high\-performance compute algorithms, models, and simulations along with their applications to solve complex business problems at client sites
- Work with large\-scale datasets and utilize data preprocessing techniques to ensure high\-quality input for training and production
- Implement and maintain efficient data storage and retrieval mechanisms for models and knowledge using appropriate tools
- Justify the value of model approaches in business problems
- Collaborate with teams from both business and technical sides, including users, use case representatives, business owners, engineers, architects, and UI designers, to achieve end\-to\-end project goals and integrate into production
Travel may be required for this role. The amount of travel will vary from 0 to 100% depending on business need and client requirements.
Here’s what you need
- Minimum of 2 years of hands\-on experience across Snowflake Cortex AI \- Cortex Agents, Cortex Analyst, Cortex Search
- Minimum of 5 years of experience as a machine learning engineer or scientist, deploying models in production at scale , including monitoring, alerting, automatic bug filing and auditing.
- Minimum of 5 years of experience in applying theoretical foundations of computer science, including computer system architecture, system engineering, and programming
- Minimum of 3 years of experience in distributed computing systems and architecture that may include big data, high\-performance compute, engineering simulations, scientific compute, grid and cloud computing, distributed networks
- Minimum of 2 years of experience in building and deploying AI/ML based software to a cloud environment.
- Bachelor's degree or equivalent (minimum 12 years) work experience. (If Associate’s Degree, must have minimum 6 years work experience)
Professional Skills Requirements:
- Strong written \& verbal communication skills and ability to communicate complex technical concepts to non\-technical stakeholders
- Strong client\-facing skillsets in a consulting environment
- Strong cross\-functional skills with the ability to collaborate with a variety of internal and client\-side teams
- Entrepreneurial mindset with a curiosity and passion for emergent tech and driving innovation
Bonus Points if you have
- Proficiency in Python and python\-based AI/ML framework and familiarity with relevant libraries and frameworks (e.g., TensorFlow, PyTorch)..
- Experience working with language models like LLM's APIs and optimizing their usage for specific applications.
- Experience with the following programming languages: Python, C\+\+, Java, R, SQL
- MS or PhD in related field preferred (computer science, engineering, etc.)
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 09/30/2026\.
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 $94,400 to $293,800
Cleveland $87,400 to $235,000
Colorado $94,400 to $253,800
District of Columbia $100,500 to $270,300
Illinois $87,400 to $253,800
Maine $80,400 to $216,200
Maryland $94,400 to $253,800
Massachusetts $94,400 to $270,300
Minnesota $94,400 to $253,800
New York $87,400 to $293,800
New Jersey $100,500 to $293,800
Virginia $87,400 to $270,300
Washington $100,500 to $270,300
San Francisco, CA
Albany, NY
Arlington, VA
Atlanta, GA
Austin, TX
Beaverton, OR
Bentonville, AR
Boston, MA
Carmel, IN
Charlotte, NC
Chicago, IL
Cincinnati, OH
Cleveland, OH
Columbus, OH
Culver City, CA
Denver, CO
Des Moines, IA
Detroit, MI
Hartford, CT
Houston, TX
Irving, TX
Kirkland, WA
Miami, FL
Milwaukee, WI
Minneapolis, MN
Morristown, NJ
Mountain View, CA
Nashville, TN
New York City, NY
Oklahoma City, OK
Overland Park, KS
Philadelphia, PA
Pittsburgh, PA
Raleigh, NC
Redmond, WA
Sacramento, CA
San Diego, CA
Scottsdale, AZ
Seattle, WA
St. Louis, MO
St. Petersburg, FL
Walnut Creek, CA
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Equal Employment Opportunity Statement
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Candidates who are currently employed by a client of Accenture or an affiliated Accenture business may not be eligible for consideration.
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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 $87K-$293K range is above the median 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 Logic, Inc., 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($190K) sits 11% below the category median. Disclosed range: $87K to $293K.
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.
Logic, Inc. AI Hiring
Logic, Inc. has 16 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Positions span New York, NY, US, Mountain View, CA, US, Morristown, NJ, US. Compensation range: $196K - $434K.
Location Context
AI roles in San Francisco pay a median of $265,000 across 1,335 tracked positions. That's 23% 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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