Build a strong technical foundation with architecture built on modular/microservices, cloud/edge, API 1st, privacy by design philosophies; infrastructure concepts of containerization, orchestration, auto-scale capabilities (compute, storage, network) and infra-as-code; development concepts of automation (CI/CD, data and MLOps pipelines), code assist and sandboxes for collaboration + experimentation.
Design and deploy on GE GridNode/edge platforms, using container and microservices principles and best practices. Develop and implement strategies for optimizing performance of models in production.
Collaborate with cross-functional teams to integrate AI/ML capabilities into existing platforms and develop new intelligent business efficiency and product line solutions.
Establish architectural standards, best practices and technical guidelines for AI/ML development across the CTO organization in collaboration with GEV AI/ML partners.
Lead end-to-end project delivery from ideation through deployment, ensuring projects meet technical requirements, timelines and business objectives.
Ensure AI/ML solutions meet industry standards, regulatory requirements and cybersecurity protocols for critical energy infrastructure.
Evaluate and recommend emerging technologies and methodologies (AIML tools, platforms, vendor solutions) for their potential application to grid automation challenges and business opportunities; design, execute and demo proof-of-concepts (PoCs) to validate new AI/ML approaches and assess their feasibility for energy system applications.
Translate research insights and emerging technologies into practical solutions that can be integrated into our product lines.
Stay current with state-of-the-art developments in AI/ML, generative AI and energy systems technology through continuous monitoring of research and industry trends.
Foster a culture of innovation and learning within the team by encouraging experimentation with new technologies and knowledge sharing of industry developments.
Collaborate on resource planning and risk mitigation for complex AI/ML projects.
Coordinate cross-functional project teams, facilitating collaboration between engineering, product, operations and business stakeholders.
Drive technical decision-making for AI/ML solutions and projects including infrastructure requirements and deployment strategies for both edge computing and cloud-based solutions.
Manage and mentor a small team of AI/ML engineers, data scientists and data engineers, providing technical guidance and career development support.
Design and architect scalable AI/ML solutions, including generative AI applications, tailored to grid automation and digitalization technologies as well as business efficiency. Ensure optimal performance of these solutions across edge and cloud deployment environments.
Ensure the design and development of AI/ML solutions and project deliveries adhere to the defined framework and are scalable, high-performant, maintainable, accurate and reliable.
Requirements
python
tensorflow
spark
aws
phd
10 years
Excellent communication, presentation, organization and documentation skills.
Experience with electrical power systems T&D assets, grid automation, SCADA systems, utility and industrial customers, communications infrastructure, etc.
Understanding of digital substations, IEC standards & protocols, transmission and distribution automation segment & solutions and virtualization.
Root cause analysis skills, trouble shooting and debugging skills using tools such as Wireshark, TCPDump and other Linux and Windows system tools.
Have a combination of GPU experience, Spark and Scala to build and optimize high-performance, scalable, distributed computing solutions for data-intensive tasks; familiarity with fault tolerance and high-availability requirements for mission-critical applications.
Strong background in edge computing, IoT deployments and cloud platforms (AWS, Azure, GCP).
Experience with DevOps, data pipelines, Azure ML registry, deployment methods (Docker, K8s, etc.).
Proven expertise in machine learning frameworks (TensorFlow, PyTorch, Scikit-learn, etc.) and generative AI technologies (LLMs, SLMs, diffusion models, GANs).
Proven experience in applying AI/ML frameworks/workflows, AI/MLOps and CI/CD using cloud-native and on-prem development and deployment in operational technology/industrial automation environments.
Track record of applying research insights to solve real-world business problems and deliver commercial solutions; ability to balance innovation with practical implementation constraints and business requirements.
Knowledge of regulatory frameworks and cybersecurity requirements for energy sector applications.
Hands-on professional experience in developing and testing AI/ML algorithms and demonstrated professional experience with grid/physics models in power system simulation tools, MATLAB/PSCAD; as well as power system analysis SW such as PSS/E, Digsilent or equivalent.
Understanding of industrial IoT, edge computing requirements and real-time data processing in critical infrastructure environments.
Team player, problem solver, positivity and a Can-do attitude.
Excellent communication skills with ability to translate complex technical concepts to diverse audiences including leadership and non-tech stakeholders.
Experience developing and implementing ML models using cloud MLOps pipelines such as AWS Sagemaker, Azure ML, Google VertexAI, Dataiku Cloud or equivalent.
5+ years of people management experience with demonstrated ability to lead high-performing technical teams.
Ability to multi-task in a fast-paced multi-cultural environment.
Minimum of 10 years of hands-on experience in AI/ML development with 8+ years in architectural roles
Experience with reinforcement learning, optimization algorithms or control systems.
Experience with time-series analysis, signal processing, load forecasting and predictive modeling relevant to energy systems and grid operations.
Experience with Linux virtualized system deployment using VM, Hypervisor (EsXi, KVM, Xen, etc.), Docker and container orchestration tools.
Expertise of GraphDB, SQL/NoSQL, MS Access databases.
Proven track record of successfully delivering complex AI/ML projects from conception to deployment.
Proficiency in programming languages including Python, C# or C++ as well as scientific programming + simulation tools such as MATLAB or R.
Proven experience designing solutions that include the full AI/ML project lifecycle: data acquisition (real-time/streaming, batch and response/request), data quality assurance + engineering, model selection and evaluation, tuning, testing, deployment, maintenance and evolution.
PhD OR Master’s with a minimum of 5 years equivalent professional experience, in Computer Science, Electrical Engineering, Data Science or related technical field.
Benefits
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Training + Development
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Interview process
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Visa Sponsorship
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Security clearance
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Company
Overview
April 2024
Founded
The company emerged from the spin-off of GE's energy units in April 2024.
>$10B Quarterly Revenue
Revenue Growth
Achieves over $10 billion in quarterly revenue, driven by demand for power infrastructure and digital solutions.
$3B
Wind Turbine Backlog
Maintains a significant backlog in wind turbine orders, reflecting strong market demand.
25%
Global Electricity Supply
Contributes to generating 25% of the world’s electricity through its installed turbines and grids.
Traces roots back to Edison and Alstom, merging power, renewable, digital & financial wings.
Headquartered in Cambridge, MA, crafts large-scale gas turbines, SMRs, wind turbines, hydro and grid tech to fuel economies.
On the nuclear front, advancing small modular reactors (like BWRX‑300) in partnership with utilities and supporting semiconductor projects.
Wind prowess spans onshore, offshore and blade making—with key sites like Dogger Bank offshore and blade plants in Spain.
Electrification arm tackles grid stability: HVDC, transformers, storage, conversion, plus GridOS software powering smarter infrastructure.
Weaves finance and consulting through energy-infrastructure investments, funding solar farms to pipelines via GE Energy Financial Services.
Culture + Values
Relentlessly focused on advancing the world’s transition to cleaner, more sustainable energy.
Believes in working with customers, partners, and communities to create innovative energy solutions that make a meaningful difference.
Prioritizes excellence, integrity, and accountability in everything they do.
Committed to driving real change through technology and partnerships that will transform the global energy landscape.
Environment + Sustainability
2050 target
Net zero commitment
Committed to achieving net zero carbon emissions by 2050.
Focused on reducing emissions through advanced energy technologies.
Maximizing use of renewable energy sources and leveraging digital solutions for energy efficiency.
Solutions aim to decarbonize industries and help customers meet their sustainability goals.