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Position Summary
Bentley Systems is seeking a Senior Program Manager to play a key role in shaping Bentley's global AI adoption \& strategy. This position is a critical driver in our mission to embed AI into our core operations. The Senior Program Manager will help execute the company\-wide strategy to accelerate AI adoption, which will involve enabling colleagues and teams across the business to see and realize the potential of AI in their daily work, acting as a liaison to understand the unique work and culture of each business unit and bringing those insights back to the AI team.
Responsibilities
- Design and run a multi\-faceted program to drive the adoption of Bentley’s selected AI tools with direct accountability for the fulfilment of key corporate AI adoption OKRs
- Deliver upskilling programs within business functions to accelerate the company's transformation to AI\-first practices through training, workshops, and showcases
- Build and sustain community and champion networks that prompt the grass\-roots adoption of Bentley’s selected AI toolset and create a culture of curiosity, experimentation, and support
- Act as the voice of the business by translating organizational needs, workflows, and cultural realities into insights that shape the AI roadmap and tool selection
- Partner with the digital workplace function on enablement that reflects current tool capability and feeds tool decisions back from the field
- Contribute to vendor strategy and tool evaluation, including how external AI partners and consultancies are leveraged at Bentley
- Build deep, trust\-based relationships across business units to understand how teams operate and where AI fits naturally
- Operate as part of a cross\-functional team co\-accountable to shared OKRs, including the CIO, CHRO, Digital Workplace Tool leads, Enterprise Architecture, and AI Engineering teams
Qualifications
- 10\+ years of proven experience in a role focused on change or program management within a large, complex organization
- A strong track record of executing large\-scale, cross\-functional programs, with a focus on communication and stakeholder management
- Experience driving the adoption of new, transformative technology (AI experience is a plus) using established change management frameworks
- Excellent ability to communicate complex ideas simply and persuasively to a diverse audience
- A genuine desire to learn about the different ways people work across the company and experience leading with listening to understand the unique needs of different global teams
- Proven ability to build trust and credibility with a wide range of stakeholders, operating effectively through influence rather than direct authority
What We Offer
- A great Team and culture – please see our colleague video.
- An exciting career as an integral part of a world\-leading software company providing solutions for architecture, engineering, and construction \- watch this short documentary about how we got our start.
- An attractive salary and benefits package.
- A commitment to inclusion, belonging and colleague wellbeing through global initiatives and resource groups.
- A company committed to making a real difference by advancing the world’s infrastructure for better quality of life, where your contributions help build a more sustainable, connected, and resilient world. Discover our latest user success stories for an insight into our global impact.
\#LI\-SH1
\#LI\-Remote
About Bentley Systems
Around the world, infrastructure professionals rely on software from Bentley Systems to help them design, build, and operate better and more resilient infrastructure for transportation, water, energy, cities, and more. Founded in 1984 by engineers for engineers, Bentley is the partner of choice for engineering firms and owner\-operators worldwide, with software that spans engineering disciplines, industry sectors, and all phases of the infrastructure lifecycle. Through our digital twin solutions, we help infrastructure professionals unlock the value of their data to transform project delivery and asset performance. www.bentley.com
Equal Opportunity Employer:
Bentley is an equal opportunity employer and considers all qualified applicants for employment without regard to race, color, sex, sexual orientation, gender identity, disability, pregnancy, protected veteran status, religion, national origin, age, genetic information or any other protected characteristic. This commitment extends to all aspects of employment, including, but not limited to, hiring, placement, promotion, compensation, and training. Know Your Rights as an applicant under the law.
Bentley Policy on EEO, Affirmative Action and Pay Transparency Non\-Discrimination
Bentley participates in e\-Verify / Bentley participate in e\-Verify / Right to Work Notice
Request an Accommodation:
As an Equal Opportunity Employer, Bentley is committed to providing reasonable accommodations to applicants with disabilities. We encourage you to request a reasonable accommodation if you are not able to fully use or access our online application system. You can make an accommodation request by calling 610\-458\-5000 or sending us an email at [email protected]
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 Bentley Systems, 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. Senior-level AI roles across all categories have a median of $227,400.
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.
Bentley Systems AI Hiring
Bentley Systems has 2 open AI roles right now. They're hiring across AI Software Engineer, AI/ML Engineer. Based in PA, US.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 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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