招聘

Full-Stack Software Engineer & Scientist (Virtual Sensing)
Boston, MA; Flexible / Remote
·
Remote
·
Full-time
·
2w ago
Compensation
$89,300 - $148,700
Benefits & Perks
•Healthcare
•401(k)
•Learning Budget
•Parental Leave
•Mental Health
•Healthcare
•401k
•Learning
•Parental Leave
•Mental Health
Required Skills
MATLAB
Go
Rust
Python
HTML
CSS
TypeScript
React
Angular
Kubernetes
Apache Kafka
MQTT
gRPC
Prometheus
Grafana
Control Theory
Signal Processing
Kalman Filters
AI/ML
Job Description Summary
Full-Stack Software Engineer & Science (Virtual Sensing) - Decentralized Grid Operations
We're building the foundation for next-generation decentralized grid operations-distributing intelligence to the resilient edge to enable autonomous, self-healing, and adaptive grid management. As a Full-Stack Software Engineer & Scientist (Virtual Sensing), you'll develop edge-native software, advanced virtual sensing algorithms, and intelligent control systems that bridge physics, AI, and distributed computing. This is a hands-on, critical role in a startup-style, fast-moving environment where ideas turn into reality quickly. You will report to the Electrification Chief Architect within the CTO organization. You'll work across simulation, real-time edge deployments, and field validation to transform how the grid senses and responds.
Job Description:
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Key Responsibilities
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Build and deploy edge-native software components for decentralized operation, sensing, and control.
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Develop federated data pipelines that allow distributed nodes to collaborate securely without central coordination.
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Develop and deploy robust virtual sensing algorithms to infer critical power grid parameters (e.g., voltage stability, phase angles, line temperature, asset health) from limited sensor data available at the grid edge.
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Deploy adaptive edge intelligence and control logic at the edge, enabling real-time grid intelligence with minimal latency and network
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Collaborate with power systems engineers to validate virtual sensor accuracy against physical models and real-world data from substations, field devices, and peer sensors.
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Integrate field data sources (SCADA, PMUs, DER controllers) and IoT protocols/networks (Lo Ra, MQTT, DNP3, Modbus).
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Support experimentation and prototyping in simulation environments and customer's test sites.
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Collaborate with systems and data engineers to close the loop between simulation and live operations.
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Collaborate with cross-functional teams to integrate AI/ML capabilities, federated control frameworks, and digital twins into next-generation grid platforms.
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Work closely with data scientists, control engineers, infrastructure specialists and Customers, to integrate software with physical grid systems.
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Critical Technical skills and experience Requirements
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Working experience with Transmission and distribution , and federated architectures, and resilient edge software.
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Strong skills in script writing using MATLAB, Go, Rust, Python (backend/edge) and HTML, CSS, TypeScript/React or Angular(UI).
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Deep Experience with Kubernetes/K3s, Kafka/NATS, MQTT, gRPC, Pulsar InfluxDB/TimescaleDB, and observability stacks (Prometheus, Grafana).
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Solid understanding of control theory and signal processing techniques for application in virtual sensing systems, such as Kalman filters or other state estimation methods.
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Experience in integrating virtual sensor outputs into existing control loops for real-time monitoring, process optimization, and predictive maintenance applications.
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Ability to evaluate and test the performance and reliability of developed virtual sensors.
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Skilled in interpreting complex sensor data outputs and translating insights into actionable virtual sensor models.
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Work experience with Digital Twin / Simulation modeling: OpenFMB, Modelica, graph-based modeling) and AI/ML model algorithms.
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Hands-on work with AI/ML models in production environments.
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Work experience in federated architectures, and resilient edge software applied to Transmission & Distribution application.
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Preferred Background
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Master's degree electrical engineering or computer science with a minimum of 5 years' experience in the power or industrial system domain.
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Minimum 3+ years of hands-on experience in building real-time or simulation-based system.
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Proven experience in software engineering with, Automation, distributed/federated data processing.
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Track record of building, deploying, and scaling complex software systems.
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Familiarity with grid operations, DER management, and industrial IoT/IIoT.
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Experience with real-time or cyber-physical systems requiring low latency and high reliability.
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Knowledge of grid standards or protocols (e.g., IEEE 2030.5, IEC 61850, OpenFMB) is a strong plus.
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Familiarity with power system modeling and simulation tools such as EMT platforms (e.g. PSCAD, RTDS, Opal-RT) is a plus.
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Behavioral Skills
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Innovation in practice delivers measurable, customer-validated outcomes.
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Entrepreneurial mindset: bias for action, curiosity for what's possible, and cross-functional collaboration.
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Cross-functional collaboration across software, controls, and hardware.
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Comfortable working in agile, mission-oriented teams.
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Communication: Explains complex concepts clearly; welcomes feedback and alternative viewpoints.
Additional Information:
GE Vernova offers a great work environment, professional development, challenging careers, and competitive compensation. GE Vernova is an Equal Opportunity Employer. Employment decisions are made without regard to race, color, religion, national or ethnic origin, sex, sexual orientation, gender identity or expression, age, disability, protected veteran status or other characteristics protected by law.
GE Vernova will only employ those who are legally authorized to work in the United States for this opening. Any offer of employment is conditioned upon the successful completion of a drug screen (as applicable).
Relocation Assistance Provided: No
- This is a remote position
Application Deadline: February 28, 2026
For candidates applying to a U.S. based position, the pay range for this position is between $89,300.00 and $148,700.00. The Company pays a geographic differential of 110%, 120% or 130% of salary in certain areas. The specific pay offered may be influenced by a variety of factors, including the candidate's experience, education, and skill set.
Bonus eligibility: discretionary annual bonus.
This posting is expected to remain open for at least seven days after it was posted on January 21, 2026.
Available benefits include medical, dental, vision, and prescription drug coverage; access to Health Coach from GE Vernova, a 24/7 nurse-based resource; and access to the Employee Assistance Program, providing 24/7 confidential assessment, counseling and referral services. Retirement benefits include the GE Vernova Retirement Savings Plan, a tax-advantaged 401(k) savings opportunity with company matching contributions and company retirement contributions, as well as access to Fidelity resources and financial planning consultants. Other benefits include tuition assistance, adoption assistance, paid parental leave, disability benefits, life insurance, 12 paid holidays, and permissive time off.
GE Vernova Inc. or its affiliates (collectively or individually, "GE Vernova") sponsor certain employee benefit plans or programs GE Vernova reserves the right to terminate, amend, suspend, replace, or modify its benefit plans and programs at any time and for any reason, in its sole discretion. No individual has a vested right to any benefit under a GE Vernova welfare benefit plan or program. This document does not create a contract of employment with any individual.
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About GE Vernova
Reviews
3.8
34 reviews
Work Life Balance
3.7
Compensation
3.7
Culture
3.8
Career
3.7
Management
3.6
77%
Recommend to a Friend
Pros
Good work-life balance and flexible environment
Opportunity for career growth
Competitive compensation and benefits
Cons
Room for improvement in processes
Internal communication could improve
Some organizational bureaucracy
Salary Ranges
309 data points
Junior/L3
Junior/L3 · Business Analyst
0 reports
$92,460
total / year
Base
-
Stock
-
Bonus
-
$78,591
$106,329
Interview Experience
4 interviews
Difficulty
3.3
/ 5
Duration
14-28 weeks
Experience
Positive 0%
Neutral 50%
Negative 50%
Interview Process
1
HR Interview
2
Digital Interview
3
Technical Rounds
4
Hiring Manager Interview
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