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About This Role
Senior Research Engineer, Battery \& Digital Manufacturing
Location: Greensboro, North Carolina
Company: Soelect, Inc.
Employment Type: Full Time
About Soelect
Soelect, Inc. is an advanced battery technology company developing next generation lithium metal anodes, advanced electrode manufacturing technologies, and AI enabled material intelligence solutions.
Our technology portfolio includes Lithium\-X®, an advanced lithium metal anode platform for high energy density batteries, and Pen\-AI™ Material Intelligence, an AI based material intelligence platform designed to convert image derived information into quantitative and actionable material insights.
Soelect is expanding its R\&D activities into Digital Twin, virtual factory, smart manufacturing, autonomous manufacturing, and AI integrated battery production systems.
We are seeking a Senior Research Engineer, Battery \& Digital Manufacturing who can connect battery science and manufacturing with emerging digital technologies and lead multidisciplinary R\&D programs from concept through demonstration.
Position Overview
The Senior Research Engineer will lead and support advanced R\&D programs involving:
- Battery materials and electrochemical systems
- Lithium metal and next generation battery technologies
- Electrode manufacturing
- Digital Twin technology
- Virtual factory development
- Smart manufacturing
- AI enabled manufacturing
- Manufacturing process modeling and optimization
- Closed loop and autonomous manufacturing
- Federal and commercial R\&D programs
This is not a conventional battery research position.
The successful candidate will work at the intersection of battery science, manufacturing engineering, data, AI, process modeling, and Digital Twin technology.
The engineer will work closely with Soelect's battery scientists, process engineers, AI development team, external research institutions, customers, and government partners to develop next generation intelligent battery manufacturing technologies.
Key ResponsibilitiesBattery Materials and Process Engineering
- Lead and support R\&D activities involving lithium metal batteries, lithium ion batteries, solid state batteries, electrode materials, and advanced battery manufacturing.
- Design experiments to understand relationships among materials, manufacturing parameters, electrode structures, and electrochemical performance.
- Analyze electrode properties including thickness, loading, uniformity, morphology, porosity, surface characteristics, and other material or process related parameters.
- Establish relationships between manufacturing conditions, material characteristics, cell performance, and manufacturing quality.
- Support development and scale up of advanced electrode and lithium metal manufacturing processes.
- Apply statistical and physics based approaches to analyze manufacturing variability and identify critical process parameters.
Digital Twin and Virtual Factory Development
- Develop Digital Twin concepts for battery material and electrode manufacturing processes.
- Build relationships between physical manufacturing equipment, process parameters, material characteristics, quality measurements, and virtual process models.
- Support development of virtual representations of manufacturing equipment, process flows, materials, and production environments.
- Integrate experimental and manufacturing data into Digital Twin frameworks.
- Develop models capable of predicting manufacturing outcomes based on process and material inputs.
- Support development of virtual factory environments for process simulation, optimization, equipment interaction, and manufacturing scenario evaluation.
- Compare predicted Digital Twin outputs with physical experimental and manufacturing results.
- Develop strategies for continuously improving virtual models using physical manufacturing data.
Smart and Autonomous Manufacturing
- Develop advanced manufacturing concepts integrating process equipment, machine vision, material intelligence, Digital Twin models, and manufacturing control systems.
- Support integration of Pen\-AI™ Material Intelligence with manufacturing equipment and process data.
- Develop relationships among image derived material descriptors, manufacturing parameters, material properties, and downstream battery performance.
- Participate in development of real time manufacturing quality assessment and process optimization methodologies.
- Develop frameworks for converting manufacturing and material intelligence into actionable process decisions.
- Support development of closed loop manufacturing architectures in which material and process information can be used to dynamically optimize manufacturing conditions.
- Investigate advanced control approaches including Model Predictive Control and other data driven or physics informed manufacturing control methodologies.
- Contribute to Soelect's long term development of increasingly autonomous battery manufacturing systems.
Modeling, Data, and AI Integration
- Work with AI and software engineers to integrate physical manufacturing knowledge into data driven models.
- Develop physics based, empirical, statistical, or hybrid models describing battery manufacturing processes.
- Analyze relationships among process parameters, material descriptors, manufacturing quality, and electrochemical performance.
- Support development of physics informed and data driven predictive models.
- Define technically meaningful input and output parameters for AI and Digital Twin models.
- Evaluate model accuracy against experimental measurements and physical manufacturing data.
- Translate battery science and manufacturing knowledge into engineering requirements for AI, software, and Digital Twin development.
Research and Technology Development
- Identify emerging technologies relevant to next generation battery manufacturing.
- Develop new research concepts involving battery materials, Digital Twin technology, AI, smart manufacturing, and autonomous manufacturing.
- Design and execute experiments to validate new manufacturing concepts.
- Analyze experimental data and communicate technical conclusions.
- Prepare technical reports, research presentations, invention disclosures, and supporting documentation.
- Contribute to intellectual property development and patent activities.
- Support technical publications and conference presentations when appropriate.
Program and Project Management
The Senior Research Engineer will also play an important role in managing multidisciplinary R\&D programs.
Responsibilities include:
- Lead technical workstreams for internal, customer funded, and federally funded research programs.
- Develop technical objectives, Statements of Work, milestones, schedules, and deliverables.
- Coordinate activities among scientists, engineers, software developers, universities, national laboratories, customers, and government collaborators.
- Track technical progress, schedules, risks, budgets, milestones, and deliverables.
- Lead technical meetings and program reviews.
- Prepare technical progress reports and presentations.
- Identify technical risks and develop appropriate mitigation strategies.
- Support proposal development for new government and commercial R\&D programs.
- Translate research concepts into executable engineering programs.
- Drive projects from initial concept through experimental validation, prototype demonstration, and manufacturing implementation.
Federal Research Programs
The successful candidate may participate in research programs involving organizations such as:
- U.S. Department of Defense
- U.S. Department of Energy
- DARPA
- U.S. Air Force
- U.S. Army
- U.S. Navy
- National laboratories
- Universities and other research institutions
Responsibilities may include technical proposal development, experimental planning, milestone management, technical reporting, government program reviews, and coordination with external research partners.
Required Qualifications
- Master's degree or Ph.D. in Materials Science, Chemical Engineering, Mechanical Engineering, Electrical Engineering, Chemistry, Physics, Manufacturing Engineering, or a related technical discipline.
- Strong technical background in battery materials, battery manufacturing, electrochemistry, advanced materials, or related technologies.
- Minimum 5 years of relevant industrial, research, or engineering experience.
- Strong understanding of experimental design, data analysis, and engineering problem solving.
- Ability to understand manufacturing processes and connect physical phenomena with quantitative models.
- Experience managing multidisciplinary technical projects.
- Strong technical writing and presentation skills.
- Ability to communicate effectively with scientists, engineers, software developers, management, customers, and external research partners.
- Ability to independently lead technical projects while working effectively within multidisciplinary teams.
Preferred Qualifications
Experience in several of the following areas is highly desirable:
- Lithium metal batteries
- Lithium ion batteries
- Solid state batteries
- Battery electrode materials
- Electrode manufacturing
- Roll to roll manufacturing
- Dry electrode processing
- Battery cell fabrication and electrochemical characterization
- Digital Twin development
- Virtual factory or manufacturing simulation
- Smart manufacturing
- Industry 4\.0 manufacturing systems
- AI enabled manufacturing
- Machine vision
- Manufacturing data analytics
- Physics based modeling
- Physics informed machine learning
- Process simulation
- Model Predictive Control
- Closed loop process control
- Autonomous manufacturing
- Manufacturing execution systems
- Process equipment integration
- Federal R\&D programs
- SBIR or STTR programs
- DoD or DOE research programs
- Technical proposal development
- Program and project management
Experience with programming, modeling, or engineering tools such as Python, MATLAB, COMSOL, ANSYS, MATLAB Simulink, CAD, process simulation software, or Digital Twin platforms is highly desirable.
Ideal Candidate
We are looking for someone who thinks beyond conventional battery R\&D.
The ideal candidate understands that the future of battery manufacturing will increasingly connect:
Materials → Process → Equipment → Data → Material Intelligence → Digital Twin → Process Control → Autonomous Manufacturing
This individual should be capable of understanding the physical science of battery materials while also seeing how manufacturing data, AI, modeling, and Digital Twin technologies can be integrated into a unified manufacturing intelligence framework.
The successful candidate should be intellectually curious, technically rigorous, highly organized, and comfortable leading research in areas where established solutions may not yet exist.
Why Join Soelect
This position provides an opportunity to work at the convergence of next generation battery technology, artificial intelligence, Digital Twin technology, and advanced manufacturing.
Rather than working on only one component of battery development, the Senior Research Engineer will have the opportunity to participate in building an integrated technology platform connecting physical battery manufacturing with digital intelligence and autonomous process control.
The position will work directly with company leadership, internal R\&D teams, external research institutions, commercial partners, and U.S. Government organizations.
For an engineer interested in helping define how future battery factories will be designed, modeled, monitored, and autonomously controlled, this position offers a unique multidisciplinary research environment.
Soelect, Inc. is an Equal Opportunity Employer. Employment decisions are based on qualifications, merit, and business needs without regard to legally protected characteristics.
Job Type: Full\-time
Pay: $78,000\.00 \- $99,000\.00 per year
Benefits:
- 401(k) matching
- Dental insurance
- Health insurance
- Life insurance
- Paid time off
- Vision insurance
Education:
- Doctorate (Required)
Language:
- Korean or Chinese (Required)
Work Location: In person
Salary Context
This $78K-$99K range is in the lower quartile for Research Engineer roles in our dataset (median: $207K across 63 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 4,317 AI roles we're tracking, Research Engineer positions make up 2% of the market. At Soelect Inc., 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 $272,100 based on 227 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($88K) sits 67% below the category median. Disclosed range: $78K to $99K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and AI Engineering Manager ($244,000). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Soelect Inc. AI Hiring
Soelect Inc. has 1 open AI role right now. They're hiring across Research Engineer. Based in Greensboro, NC, US. Compensation range: $99K - $99K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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