Interested in this AI/ML Engineer role at Celonis?
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About This Role
Celonis is the global leader in Process Intelligence and the pioneer of Process Mining technology. As one of the world's fastest\-growing enterprise SaaS companies, we are changemakers pushing the boundaries of what's possible. We invest heavily in advanced AI capabilities—specifically our Process Intelligence Graph—to turn data insights into immediate business action. We believe there is a massive opportunity to unlock global productivity and sustainability by placing intelligence at the core of every business process. Join our mission to make processes work for people, companies, and the planet.
The Team: Global Value Engineering Center of Excellence
Become an integral part of our international, diverse, and dynamic Global Value Engineering Center of Excellence. Our Value Engineers are trusted advisors who operate at the intersection of technology and business, partnering with sales and customers to develop compelling Celonis\-powered solutions and prototypes that address critical business challenges.
We foster a fast\-paced, collaborative environment built on trust, continuous learning, and mutual growth through coaching and mentorship.
The Role: Launch Your Enterprise Software Career
Kickstart your career in enterprise software through our immersive 12\-month Orbit Program. This program offers a unique blend of client engagements, dedicated mentorship, and a strong team culture where collaboration and shared success are paramount. You'll leverage your technical background to develop essential Value Engineering skills, build lasting client relationships, and establish yourself as a trusted advisor, paving the way for significant career growth within Celonis.
The Orbit Program is structured into classes and cohorts, with start dates organized twice a year. You'll likely join a cohort with a summer start date (June/July) or a winter start date (January/February), allowing you to grow and develop alongside your cohort peers.
Your journey begins with comprehensive onboarding and enablement, followed by hands\-on experience in customer engagements and targeted training. You will gain expertise in:
- Building and coding within the Celonis platform (SQL and PQL)
- Scoping business and technical requirements
- Effectively handling objections
- Developing industry domain knowledge
- Delivering impactful presentations and demos
- Understanding value selling and realization
- Managing customer accounts
What You'll Do:
- Consult with customers using systems analysis to identify core business challenges and determine how Celonis can drive impactful solutions.
- Present the Celonis AI value proposition and vision to customers and align our solutions with their AI strategy.
- Prototype and develop AI solutions tailored to customer needs, including both traditional machine learning and LLM\-based solutions.
- Execute proof projects showcasing the value of Celonis Process Intelligence in the context of our customers' strategy, business initiatives, and challenges
- Develop and modify Celonis data models using SQL and analyses using PQL (Celonis proprietary language) to create compelling prototypes that address customer pain points.
- Lead proof\-of\-concept projects within the Celonis platform to demonstrate tangible value aligned with customer strategies and needs.
- Collaborate with customers and partners to design and build Celonis solutions that ensure a positive return on investment.
- Establish strong, trusted advisor relationships with customers through your technical expertise, product knowledge, and business acumen.
Articulate and quantify strategic business value for customers, delivering impactful presentations to senior executives.
- Partner with the implementation services team to ensure successful value realization for customers.
- Identify opportunities for innovation and collaborate with Product \& Engineering teams to influence future product development.
- Contribute to the Global Value Engineering Center of Excellence by creating reusable collateral, best practices, and tools.
What You Need:
- Bachelor's degree in Business, Engineering, Data Analytics, Economics, Computer Science, Mathematics, or a related STEM field.
- Extensive internship experience or 1\-3 years of full\-time work experience.
- Hands\-on experience with at least one of the following: SQL, Microsoft Power BI, Tableau, or another data analytics solution.
- A strong passion for technology, big data, and its transformative potential.
- Excellent analytical and creative problem\-solving skills, with the ability to apply technology to business challenges.
- A customer\-centric mindset with a focus on delivering value.
- The ability to build relationships with senior management and influence decision\-making.
- Confidence in presenting to diverse audiences.
- A proactive approach to problem\-solving, with a willingness to learn from mistakes.
- Curiosity, self\-motivation, a commitment to continuous learning, and the desire to thrive in a fast\-paced, high\-growth environment.
- Strong organizational skills with the ability to prioritize and execute on deadlines.
- Excellent communication skills and fluency in English.
Bonus Points:
- GPA of 3\.6 or higher.
- Degree in Industrial Engineering or a similar field.
- Degrees combining technical (e.g., Computer Science, Data Science) and business (e.g., Economics, Marketing) skills.
- Experience with Python and Machine Learning.
- Customer\-facing, customer success/service, or sales experience.
- Experience with Proof of Concept projects.
- Supply Chain process experience (Procurement, Order Management, Inventory Management, Production).
- Finance process experience (Accounts Payable, Accounts Receivable).
- Experience modeling ROI and TCO for business case justification.
- Experience with SAP and/or Oracle.
- Fluency in French and/or Spanish.
Visa sponsorship is not offered for this role.
What Celonis can offer you:
- Pioneer Innovation: Work with the global leader in Process Mining and the Process Intelligence Graph to shape the future of AI\-driven business operations.
- Ownership from Day 1: Every full\-time "Celonaut" is an owner, receiving Restricted Stock Units (RSUs) and merit\-based refresh grants.
- Unrivaled Family Support: Benefit from our inclusive parental leave policy—24 weeks of fully paid leave for primary carers and 12 weeks for supporting carers, available from your first day of employment.
- Work\-Life Integration: Enjoy Unlimited PTO (in applicable regions) and generous PTO globally, as well as a flexible hybrid work model that balances remote focus with vibrant office collaboration.
- Continuous Growth: Elevate your skills through our 70\-20\-10 learning framework, mentorship programs, and access to a dedicated learning platform.
- Holistic Well\-being: Prioritize your health with subsidized Wellhub memberships, mental health counseling, and dedicated "Wellness Weeks" that prioritize work/life balance.
- Drive Sustainability: Participate in annual Impact Days, where you receive paid time off to volunteer for community and environmental causes with your local office, or virtually.
- Global Inclusion \& Belonging: Find community through our Inclusion Think Tank and participate in our annual Inclusion Days, ensuring every voice is heard and valued.
- Value\-Driven Impact: Join a mission\-led organization where our core values—Live for Customer Value, The Best Team Wins, We Own It, and Earth Is Our Future—drive every decision.
About Us:
Celonis makes processes work — for people, companies, and the planet. Powered by process mining and AI, the Celonis Process Intelligence Platform integrates process data and business context to create a living digital twin of business operations. We enable thousands of companies worldwide to understand how their business actually runs and, together with their partners, build intelligent solutions that transform and continuously improve the way they operate — unlocking billions in value. Celonis is headquartered in Munich, Germany, and New York City, USA, with more than 20 offices worldwide.
Get familiar with the Celonis Process Intelligence Platform by watching this video.
Celonis Inclusion Statement:
At Celonis, we believe our people make us who we are and that "The Best Team Wins". We know that the best teams are made up of people who bring different perspectives to the table. And when everyone feels included, able to speak up and knows their voice is heard \- that's when creativity and innovation happen.
Your Privacy:
Any information you submit to Celonis as part of your application will be processed in accordance with Celonis' Accessibility and Candidate Notices
By submitting this application, you confirm that you agree to the storing and processing of your personal data by Celonis as described in our Privacy Notice for the Application and Hiring Process.
Please be aware of common job offer scams, impersonators and frauds. Learn more here.
Salary Context
This $90K-$95K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Celonis, 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 $218,750 based on 3,817 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $120,000. This role's midpoint ($92K) sits 58% below the category median. Disclosed range: $90K to $95K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Celonis AI Hiring
Celonis has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $95K - $95K.
Location Context
AI roles in New York pay a median of $220,000 across 1,045 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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