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About This Role
Location: Hybrid – Exton, PA/Philadelphia
Position Summary
Bentley is building a specialized AI Code Modernization team focused on applying advanced AI techniques to transform large-scale engineering software systems. This team is responsible for developing AI-powered modernization pipelines that help migrate and evolve complex legacy applications, including scientific and engineering codebases, into modern cloud-native architectures.
This is a highly applied engineering role. You will work at the intersection of software engineering, AI systems, developer tooling, and modernization automation. Success is measured through production impact, scalability, reliability, and the ability to accelerate modernization across Bentley's portfolio of products.
What You'll Do
- Design and build AI-driven software transformation pipelines for large-scale legacy codebases.
- Develop and improve agentic AI workflows that support code understanding, refactoring, validation, and modernization.
- Modernize existing desktops, simulation, and engineering applications into cloud-native services and modern architectures.
- Use loop engineering approach to improve quality, reduce costs and accelerate the conversion/test/correction process.
- Evaluate and improve AI systems for accuracy, reliability, performance, observability, and cost efficiency.
- Create automated tooling, validation frameworks, and testing strategies to ensure correctness of transformed code.
- Partner with architects, product leaders, and engineering teams to identify modernization opportunities and drive adoption.
- Contribute to technical direction, best practices, and reusable modernization frameworks that can be leveraged across Bentley.
- Help establish and share expertise in a rapidly evolving AI domain.
- This role does not require travel.
- Requires sitting or standing at will while performing work on a computer (or any other physical requirements).
Required Qualifications
- Strong software development background with ownership of production systems.
- Experience building, maintaining, or modernizing complex software applications.
- Demonstrated understanding of software performance, reliability, scalability, testing, and failure modes.
- Practical experience applying AI, machine learning, inference pipelines, optimization techniques, or intelligent automation to real-world problems.
- Systems-oriented mindset with the ability to bring structure to ambiguous technical challenges.
- Experience designing pipelines, feedback loops, iterative systems, or large-scale developer tooling.
- Strong communication skills and the ability to collaborate with technical and non-technical stakeholders.
Preferred Qualifications
- Experience with Rust, C++, C, or Fortran.
- Experience modernizing legacy systems or migrating applications to cloud-native architectures.
- Background in DevOps, CI/CD pipelines, testing frameworks, simulation systems, benchmarking, or validation tooling.
- Experience working with engineering, scientific, mathematical, or simulation software.
- Familiarity with cloud platforms and distributed systems.
- Interest in emerging AI development patterns, agentic workflows, and software transformation technologies.
What Success Looks Like
Successful candidates will demonstrate the ability to:
- Modernize existing systems rather than only building greenfield solutions.
- Make thoughtful tradeoffs between performance, reliability, latency, maintainability, and cost.
- Apply AI in practical, production-oriented ways.
- Navigate ambiguity while maintaining engineering rigor.
- Build systems that continuously improve and scale over time.
Who Thrives in This Role
- Engineers who enjoy applying AI to real-world engineering challenges and measuring success through production impact.
- Builders who are comfortable working within large, evolving codebases.
- Generalists who enjoy moving across languages, tools, and technical domains.
- Engineers who embrace continuous learning as AI capabilities and modernization approaches evolve.
This is a Hybrid role.
What We Offer:
- Step into a collaborative work environment where ideas flourish, and teamwork propels us forward towards shared success.
- An attractive salary and benefits package.
- Bentley Impact Day: take a day off from work to volunteer with an organization of your choice.
- Celebrate milestone achievements and moments that matter through our colleague recognition award programs and our Bentley Achievers platform.
- A commitment to inclusion, belonging and colleague wellbeing through global initiatives and resource groups.
- Be part of 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.
Who We Are:
Bentley Systems (Nasdaq: BSY) is the infrastructure engineering software company. We provide innovative software to advance the world’s infrastructure – sustaining both the global economy and environment. Our industry-leading software solutions are used by professionals, and organizations of every size, for the design, construction, and operations of roads and bridges, rail and transit, water and wastewater, public works and utilities, buildings and campuses, mining, and industrial facilities. Our offerings, powered by the iTwin Platform for infrastructure digital twins, include MicroStation and Bentley Open applications for modeling and simulation, Seequent’s software for geoprofessionals, and Bentley Infrastructure Cloud encompassing ProjectWise for project delivery, SYNCHRO for construction management, and AssetWise for asset operations. Bentley Systems’ 5,200 colleagues generate annual revenues of more than $1 billion in 194 countries.
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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 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 $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Bentley Systems AI Hiring
Bentley Systems has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Exton, PA, US.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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