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About This Role
As an Embedded Computing Research Engineer at GE Vernova’s Advanced Research Center, you will develop, implement, and test edge solutions for embedded systems that power industrial automation, networking, and enterprise software platforms. You will contribute hands-on across the full development lifecycle, from concept and architecture through implementation, integration, and verification.
You will help execute multimillion-dollar research and development programs aligned with GE Vernova’s energy businesses, including Power, Wind, Electrification, and Grid Solutions, as well as U.S. Government agencies such as the Department of Defense and Department of Energy. The technologies you develop will be matured and transitioned into GE Vernova products.
You will collaborate with teams across GE Vernova, suppliers, government partners, national laboratories, universities, and strategic business partners to advance innovative embedded computing solutions. Job Description
Roles and Responsibilities
- Develop, implement, and test edge and embedded software solutions for industrial automation, networking, and enterprise software platforms.
- Serve as a technical contributor on R&D tasks, translating program requirements into robust embedded-system designs and implementation plans.
- Develop firmware and application software in C/C++ and related languages for resource-constrained, real-time embedded platforms.
- Build, execute, and automate test plans, including unit, integration, hardware-in-the-loop (HIL), and system-level verification.
- Support the maturation and transition of research prototypes into GE Vernova product platforms.
- Collaborate with cross-functional teams, suppliers, national laboratories, universities, and government partners to deliver program milestones.
- Contribute to proposals, technical reports, and program reviews for internal leadership and government sponsors.
- Apply secure-by-design principles and cybersecurity best practices throughout the embedded-software lifecycle.
Required Qualifications
- Bachelor’s, master’s, or PhD degree in Computer Science, Electrical Engineering, or a related field, with 1–5 years of relevant industry experience.
- A minimum of 1 year of professional experience in embedded systems, firmware, or edge software development.
- Demonstrated proficiency in embedded software development using C and/or C++.
- Experience with microcontrollers, microprocessors, or system-on-chip platforms and associated hardware interfaces, such as SPI, I²C, UART, CAN, or Ethernet.
- Familiarity with industrial communication protocols, such as Modbus, DNP3, OPC UA, IEC 61850, MQTT, or BACnet, and data-serialization formats such as Protocol Buffers or JSON.
- Ability to integrate existing real-time analytics, anomaly-detection capabilities, and secure data pipelines into edge and embedded applications.
- Experience developing for Linux systems or real-time operating systems.
- Experience across the embedded-systems development lifecycle, including requirements, design, implementation, testing, and debugging.
Desired Characteristics
- Familiarity with industrial control systems, programmable logic controllers, or edge controllers used in grid operations, industrial automation, or renewable-energy infrastructure.
- Experience with RTOS task scheduling, thread priorities, and strict timing deadlines.
- Full-stack programming experience delivering edge-to-cloud solutions.
- Experience developing for real-time operating systems and/or bare-metal environments.
- Experience with Docker and Kubernetes.
- Knowledge of memory management and pointer manipulation.
- Knowledge of asymmetric and symmetric multiprocessing concepts, virtual memory, and their real-time implications.
- Experience developing test automation, build scripts, and rapid prototypes using Python or other scripting languages.
- Working knowledge of embedded cybersecurity, secure boot, cryptographic primitives, or industrial control and operational technology security standards, such as IEC 62443 or NIST SP 800-82.
- Experience with JavaScript or TypeScript for front-end development and C++ or Python for integration with system-level services or embedded APIs.
- Experience with CI/CD environments such as Jenkins and GitHub Actions.
- Familiarity with multithreaded applications and computer networking.
- Familiarity with Xilinx and/or Intel (Altera) FPGA devices, tools, and ecosystems, such as ModelSim, Intel Quartus, or Xilinx Vivado.
- Knowledge of digital logic design, including finite-state machines, pipelining, and resource optimization.
- Experience with high-speed and standard interfaces such as UART, SPI, I²C, Ethernet, PCIe, or DDR.
- Experience developing testbenches and performing functional verification.
- Knowledge of clock-domain crossing techniques and metastability mitigation.
- Experience with FPGA or ASIC design using Verilog, SystemVerilog, or VHDL.
- Experience with the FPGA design flow, including simulation, synthesis, place and route, static timing analysis, and timing closure.
Eligibility Requirements
- Must be willing to work from the GE Vernova office in Niskayuna, New York
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
#LI-Remote - This is a remote position
For candidates applying to a U.S. based position, the pay range for this position is between $80,000.00 and $120,000.00. 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 October 09, 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.
Salary Context
This $80K-$120K range is in the lower quartile for Research Engineer roles in our dataset (median: $165K across 57 roles with salary data).
View full Research Engineer salary data →Role Details
About This Role
Research Engineers bridge the gap between research and production. They implement papers, build experiment infrastructure, optimize training pipelines, and make research prototypes production-ready. They're the engineers who make research work at scale.
The role sits at a unique intersection. You need to understand the math well enough to implement novel architectures correctly, and you need the engineering chops to make them run efficiently on distributed systems. When a research scientist has a breakthrough idea, you're the person who turns it from a notebook prototype into a training pipeline that runs on 256 GPUs.
Across the 9,079 AI roles we're tracking, Research Engineer positions make up 2% of the market. At GE Vernova, this role fits into their broader AI and engineering organization.
Research Engineer roles are growing as AI labs recognize that research velocity depends on engineering quality. The role is less competitive than Research Scientist (no PhD required), but the bar for engineering skill is very high. These roles are concentrated at major labs and well-funded startups.
What the Work Looks Like
A typical week involves: implementing a new attention mechanism from a recent paper, profiling and optimizing a training pipeline that's bottlenecked on data loading, building evaluation infrastructure for a new benchmark, debugging distributed training issues across a GPU cluster, and pair-programming with a research scientist on their latest experiment. The work is deeply technical.
Research Engineer roles are growing as AI labs recognize that research velocity depends on engineering quality. The role is less competitive than Research Scientist (no PhD required), but the bar for engineering skill is very high. These roles are concentrated at major labs and well-funded startups.
Skills Required
Strong software engineering fundamentals plus ML knowledge. Python, C++, and CUDA experience are common requirements. You'll need to read papers and turn ideas into working code. Distributed systems experience (especially distributed training) is highly valued. Performance optimization skills separate great candidates from good ones.
Experience with large-scale training infrastructure (FSDP, DeepSpeed, Megatron), GPU programming (CUDA, Triton), and the internals of ML frameworks (PyTorch internals, custom autograd functions) is what makes candidates stand out. The best research engineers can debug issues that span the full stack from GPU memory management to numerical precision to algorithmic correctness.
Strong postings mention the team's recent research, the infrastructure scale, and the specific technical challenges. They often list the research areas you'd support. Look for roles that emphasize both implementation quality and research understanding.
Compensation Benchmarks
Research Engineer roles pay a median of $252,000 based on 457 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($100K) sits 60% below the category median. Disclosed range: $80K to $120K.
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 AI Engineering Manager ($251,622). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
GE Vernova AI Hiring
GE Vernova has 19 open AI roles right now. They're hiring across Research Engineer, Data Scientist, AI Product Manager, AI Software Engineer. Positions span Niskayuna, NY, US, Atlanta, GA, US, Niskayuna, NY. Compensation range: $120K - $203K.
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 Research Engineer roles include Software Engineer, ML Engineer, Research Intern.
From here, career progression typically leads toward Senior Research Engineer, Research Scientist, ML Architect.
This is one of the best entry points into AI research without a PhD. Build a strong engineering portfolio with ML projects, contribute to open-source ML frameworks, and demonstrate that you can implement complex ideas correctly and efficiently. The transition to Research Scientist is possible with published first-author work, which some research engineer roles support.
What to Expect in Interviews
Technical screens test both engineering skill and research understanding. Expect coding rounds with performance-critical implementations (GPU optimization, efficient data loading). Be prepared to discuss papers relevant to the team's research area and explain how you'd implement key ideas. System design questions focus on training infrastructure: distributed training, experiment tracking, and compute resource management.
When evaluating opportunities: Strong postings mention the team's recent research, the infrastructure scale, and the specific technical challenges. They often list the research areas you'd support. Look for roles that emphasize both implementation quality and research understanding.
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).
Research Engineer roles are growing as AI labs recognize that research velocity depends on engineering quality. The role is less competitive than Research Scientist (no PhD required), but the bar for engineering skill is very high. These roles are concentrated at major labs and well-funded startups.
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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