Interested in this AI/ML Engineer role at Conduent?
Apply Now →About This Role
Through our dedicated associates, Conduent delivers mission\-critical services and solutions on behalf of Fortune 100 companies and over 500 governments \- creating exceptional outcomes for our clients and the millions of people who count on them. You have an opportunity to personally thrive, make a difference and be part of a culture where individuality is noticed and valued every day.
Summer Intern – CXM Transformation, AI \& GTM Support
Are you interested in helping shape the future of customer experience, AI\-driven solutions, and go\-to\-market strategy?
Do you enjoy solving problems, working across teams, and contributing to meaningful business initiatives?
About the Role
As a Summer Intern, you will support a variety of transformation initiatives across Customer Experience Management (CXM), AI, Product, and Go\-to\-Market (GTM) functions. You will work closely with cross\-functional teams to assist with research, solution development, documentation, presentations, market analysis, and project coordination.
This role is ideal for students interested in technology, artificial intelligence, business strategy, product development, marketing, consulting, or project management.
Potential Areas of Placement
Internship opportunities may include work in the following areas:
- Customer Experience Management (CXM) Transformation
- Artificial Intelligence (AI) \& Automation Solutions
- Product Strategy \& Solution Development
- Go\-to\-Market (GTM) Strategy \& Enablement
- Business Operations \& Project Management
- Market Research \& Competitive Intelligence
- Sales Enablement \& Solution Packaging
- Marketing Communications \& Content Development
Responsibilities
- Support go\-to\-market and product initiatives for emerging CX and AI solutions
- Assist with developing documentation, playbooks, training materials, and enablement content
- Conduct market research, competitive analysis, and solution comparisons
- Support solution development efforts across AI, automation, messaging, and customer experience capabilities
- Create presentations, graphics, and executive\-level materials to support strategic initiatives
- Assist with social media, market engagement, and industry research activities
- Help organize priorities, track project milestones, and support cross\-functional workstreams
- Collaborate with teams across marketing, legal, finance, product, and operations to support business initiatives
- Contribute ideas and recommendations that help improve processes, solutions, and customer outcomes
Responsibilities
- Currently pursuing a Bachelor's or Master's degree in Computer Science, Information Technology, Data Science, Artificial Intelligence, Business, Marketing, Communications, Analytics, Engineering, or a related field
- Strong interest in technology, AI, business transformation, customer experience, or go\-to\-market strategy
- Excellent written and verbal communication skills
- Strong analytical, research, and problem\-solving abilities
- Ability to manage multiple priorities and work effectively in a collaborative environment
- Comfortable working in a fast\-paced environment with evolving priorities
- Proficiency with Microsoft Office Suite (Excel, PowerPoint, Word)
- Experience with presentation design, research, analytics, or technical projects is a plus
- Familiarity with AI tools, automation platforms, data analysis, or software development is a plus
What You'll Gain
- Hands\-on experience supporting enterprise transformation initiatives
- Exposure to AI, automation, customer experience, and go\-to\-market strategy
- Mentorship from business and technology leaders
- Experience working on cross\-functional projects with measurable business impact
- Professional development and networking opportunities
- Exposure to emerging technologies and innovative business solutions
- The opportunity to contribute to meaningful work while building skills for future career growth
Applicants may be considered for one or more functional areas based on their background and preferences indicated during the application process.
Pay Transparency Laws in some locations require disclosure of compensation and/or benefits\-related information. For this position, actual salaries will vary and may be above or below the range based on various factors including but not limited to location, experience, and performance. In addition to base pay, this position, based on business need, may be eligible for a bonus or incentive. In addition, Conduent provides a variety of benefits to employees including health insurance coverage, voluntary dental and vision programs, life and disability insurance, a retirement savings plan, paid holidays, and paid time off (PTO) or vacation and/or sick time. The estimated hourly rate for this role is ($20\.00 per hr.).
Conduent is an Equal Opportunity Employer and considers applicants for all positions without regard to race, color, creed, religion, ancestry, national origin, age, gender identity, gender expression, sex/gender, marital status, sexual orientation, physical or mental disability, medical condition, use of a guide dog or service animal, military/veteran status, citizenship status, basis of genetic information, or any other group protected by law.
For US applicants: People with disabilities who need a reasonable accommodation to apply for or compete for employment with Conduent may request such accommodation(s) by submitting their request through this form that must be downloaded: click here to access or download the form. Complete the form and then email it as an attachment to [email protected]. You may also click here to access Conduent's ADAAA Accommodation Policy.
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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Conduent, 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 $181,170 based on 12,692 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $97,880.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Conduent AI Hiring
Conduent has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $170,000 across 1,926 positions. About 15% of all AI roles offer remote work.
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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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