Sr Software Engineer

GE Aerospace is focused on transforming aerospace engine design through AI-assisted tooling. The role involves designing, building, and maintaining software applications and services in machine learning and data pipelines to enhance engineering workflows and product outcomes.

Responsibilities:

• Define, develop, and evolve AI-enabled software products and platforms that accelerate aerospace design engineering workflows, leveraging large-scale simulation, test, and manufacturing data
• Provide hands-on technical leadership for an Agile team of 8-10 engineers, setting architecture, coding standards, and delivery practices while remaining close to implementation
• Partner with Control Title Holders and engineering stakeholders to understand product vision and translate design-engineering needs (e.g., performance, durability, operability) into software and AI capabilities
• Translate requirements into a prioritized backlog of epics/user stories, driving delivery to required timelines, quality, security, and operational standards
• Collaborate with architects and domain experts to develop and execute multi-generation technology roadmaps for AI, data, and platform modernization (e.g., simulation-data pipelines, model serving, evaluation, and governance)
• Lead the design and implementation of data/ML pipelines that ingest and curate simulation outputs (CFD/FEA/thermal/structural), test data, and engineering metadata—enabling analytics, surrogate modeling, optimization, and AI-assisted decision support
• Build and operate cloud-native services on AWS and Azure, including secure storage, scalable compute, orchestration, and MLOps capabilities (e.g., automated training, reproducibility, and model lifecycle management)
• Drive increased efficiency across teams by eliminating duplication and enabling reuse through shared data products, common APIs, feature/model registries, templates, and reference architectures
• Establish and improve engineering processes across development, sustainment, and production support—improving reliability through observability, incident response playbooks, automated remediation, and post-incident learnings
• Work cross-functionally with other business departments (engineering, manufacturing, quality, IT/security) to align dependencies, compliance requirements, and deliverables
• Drive world-class quality through rigorous SDLC practices: Lean/Agile/XP, CI/CD, automated testing, secure coding, scalability patterns, documentation-as-code, refactoring, and performance engineering
• Ensure the team has clear understanding of business direction, strategy, priorities, and measurable outcomes; communicate consistently and transparently
• Engage subject matter experts to ensure successful transfer of complex domain knowledge (e.g., physics-based modeling assumptions, boundary conditions, mesh/solver settings) into scalable software abstractions and data standards
• Write production-quality code that meets standards and delivers intended functionality using the most appropriate technologies for the project (e.g., Python, Java, C#, TypeScript—based on system needs)
• Understand performance parameters for data-intensive and AI workloads; assess and improve application performance across compute, memory, storage, and network
• Apply strong fundamentals in data structures and algorithms, implementing efficient approaches for large datasets, scientific computing workflows, and high-throughput services
• Proactively share information across the team and stakeholders with the right level of detail, strong timeliness, and clear technical rationale

Requirements:

• Bachelor's Degree from an accredited college or university (or a high school diploma / GED with a minimum of 4 years of relevant working experience)
• At least an additional 3 years of relevant working experience
• Proven experience building data platforms and ML systems for engineering/scientific data (simulation, test, telemetry, manufacturing, or similar)
• Strong cloud expertise across AWS and Azure, including architecture, security, and operations
• Experience with MLOps practices: experiment tracking, reproducible training, model registry, CI/CD for ML, automated evaluation, monitoring/drift detection, and controlled rollouts
• Experience building APIs and services for AI-powered applications (REST/gRPC), plus strong data access patterns and query optimization
• Familiarity with modern engineering data formats and workflows (e.g., time-series, large unstructured results, metadata catalogs)
• Experience with Windows and Unix/Linux development environments
• Understanding of simulation-driven design workflows and data (e.g., CFD, FEA, thermal, aero/structural analysis), including common pain points: traceability, configuration management, reproducibility, and data volume
• Experience with surrogate modeling, optimization loops, or AI-assisted design exploration is a strong plus