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Bentley Systems is forming a new, dedicated AI Systems Modernization team to lead the company’s efforts in applying advanced AI techniques—such as multi‑stage, agentic workflows—to automatically modernize academic and legacy codebases into cloud‑native services and applications. This global team will work at the intersection of AI research and real‑world software engineering, collaborating closely with product teams to support, guide, and enable AI adoption across Bentley.
Job Responsibility:
Define the technical vision and roadmap for Bentley’s AI code modernization platform
Design multi-stage, agentic AI workflows for automated code analysis, transformation, and uplift
Build and evolve LLM pipelines, including orchestration, evaluation, monitoring, and iteration
Create rigorous testing and validation strategies to ensure correctness, performance, and numerical precision
Establish quality gates and acceptance criteria for production use
Evaluate behavior across languages and runtimes to ensure fidelity of converted systems
Lead architectural decisions and help teams converge quickly on effective solutions
Balance tradeoffs between speed, cost, quality, and risk
Help hire, mentor, and guide a small team of technical experts
Enable adoption across Bentley through best practices, tooling, and education
Requirements:
Principal-level experience leading complex technical initiatives
Strong problem-solving skills and experience working with large or legacy codebases
Hands-on experience with AI-assisted development or LLM pipelines
Proficiency with cloud platforms and modern service architectures
Working knowledge of legacy languages (e.g., C, Fortran) and modern languages (e.g., Rust, .NET, C++)
Understanding of legacy and modern code patterns and how they translate across paradigms
Familiarity with numerical precision considerations across runtimes
Experience with code profiling, performance analysis, and optimization, particularly in performance-sensitive or numerically intensive systems
Understanding of High-Performance Computing (HPC) and parallel execution concepts relevant to modernizing scientific or engineering workloads
Familiarity with GPU or accelerator-based computing and how execution and memory models impact modernization feasibility and validation
Working knowledge of CUDA or similar GPU programming models, sufficient to assess transformation impact on correctness, precision, and performance
Strong communication skills and ability to influence across teams
Nice to have:
Scientific or engineering software experience (e.g., Finite Element Analysis)
Compiler theory, AST-level code analysis, or developer tooling
Experience with Azure and/or GCP
Containerization and orchestration (Docker, Kubernetes, Istio)
Application and cloud security practices
Equivalence verification or validation techniques
What we offer:
An attractive salary and benefits package
A commitment to inclusion, belonging, and colleague well-being through global initiatives and resource groups