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About This Role
Why This Role Is Critical:
As the need for nuclear energy grows, the future couldn’t be brighter. Join our vital mission to create lasting solutions for our planet’s greatest challenges. In this role, you are ready to shape the future of clean energy and make a long\-lasting global impact. With us, anticipate engaging and challenging projects that deliver innovative solutions and value\-added technologies helping power more than 38 million North American homes.
Framatome is leveraging digital technologies to enhance the safety and performance of the North American fleet. This role sits at the center of that transformation, helping translate business needs into secure, valuable digital solutions.
Within the Installed Base America (IB\-A) Business Unit, you will serve as a Full Stack Software Engineer as a subject\-matter leader. You will provide fundamental programming and consulting services for various internal customers to modernize Framatome’s business processes through digital transformation.
This role offers the opportunity to directly influence the safety and efficiency of critical energy infrastructure while developing expertise in deploying cutting\-edge applications and tools. You'll collaborate with teams ranging from field operations to senior leadership.
What You’ll Do Day\-To\-Day:
- Implement technical requirements: Write code to implement features by following specifications, and designs.
- Quality Control: Verify software through testing, code reviews, and documentation.
- Technical risk and quality ownership: Identify systemic technical risks, propose mitigation strategies, and ensure high\-reliability software in safety\-critical environments.
- Partner with Stakeholders: Lead identifying operational challenges and requirements using off the shelf products such as LLMs, ML tools, Framatome’s software portfolio, and new/emerging technologies.
- Learn: Industry best practices, time management tools, emerging technologies.
- Research: Expand team knowledge by researching emerging technologies and presenting key findings.
- Ownership: You will be asked to own part or all of one or more applications, making strategic leadership decisions, and being responsible for creating and defending software design decisions. This includes defining technical requirements, breaking down work into tasks, and guiding feature implementation from concept to completion.
- System\-level thinking: Lead architectural design and long\-term technical strategy for one or more applications.
- Leadership: Provide guidance and mentorship to newer engineers you may work alongside. This includes helping them understand best practices, reviewing their code, supporting their technical growth, and contributing to a collaborative learning environment. You may also assist in onboarding, offering technical direction, and reinforcing team standards to help junior team members succeed.
- Cross\-team influence: Drive alignment across teams and ensure engineering practices and standards are consistently applied.
- Business Impact: Translate high\-level business needs into scalable, long\-term digital solutions with measurable impact.
What You'll Bring:
- Bachelor's degree in Engineering, Business, Information Systems, Computer Science, or related technical discipline. Equivalent work experience may be considered in lieu of a degree.
- Minimum of 7 years of related experience required.
- Communication Skills: Strong written and verbal communication skills; comfortable presenting to internal customers.
- Teamwork: Ability to develop and maintain strong interpersonal relationships; work collaboratively.
- Time Management: Ability to manage tasks effectively for yourself and others.
- Expert\-level Software Development: Write code to create full stack applications using Python, C\#, TypeScript, CSS, and modern frameworks such as Vue or React.
- Experience architecting and deploying AI/ML solutions: Passion for leveraging LLMs, building AI tools and Agents to complete complex tasks.
- Experience designing and governing data architecture: Ability to document data structures, create ETLs and reports.
- Experience establishing or maturing DevSecOps practices across teams or organizations: Strong understanding of secure software development practices, including integrating security into CI/CD pipelines, automating security scans, managing vulnerabilities, and ensuring compliance throughout the development lifecycle. Experience implementing secure coding standards and collaborating with security stakeholders to drive proactive risk reduction.
- Complex Systems Upgrades \& Migrations: Experience planning and leading large scale system upgrades, platform modernizations, or technology migrations. Able to evaluate technical risk, coordinate across multiple teams, create phased migration strategies, and ensure continuity of operations for mission critical applications.
- Occasional domestic travel (\<15%) to customer sites and Framatome facilities. Periodic international travel to France, Germany, and other Framatome locations for R\&D meetings and coordination on digital initiatives
- Mobile Development: Native or React Native or other frameworks.
- Cloud Architecture: Experience designing and implementing secure, scalable cloud architectures (Azure, AWS, or similar). Ability to evaluate cloud native services, lead migrations to cloud platforms, define infrastructure patterns, and ensure resilience, performance, and cost effective operation across enterprise systems.
Total Rewards Package:
- Salary: $99,000 \- $134,000, and may also include annual incentives and performance bonuses. The base salary range is a guideline. Individual total compensation will vary based on factors such as qualifications, skill level, competencies and work location.
- Health \& Wellness: Multiple medical plan options, dental \& vision coverage, life insurance, long\- and short\-term disability, and optional supplemental plans (critical illness, accident, hospital indemnity, dependent life).
- Retirement: 401(k) with employer match.
- Paid Time Off: Up to 3 weeks of vacation, 8 sick days, and 13 paid holidays annually.
- Extra Perks: Lifestyle spending account, employer paid mental health support, education reimbursement, professional development opportunities, adoption assistance, parental leave, and employee assistance programs.
About Framatome:
Framatome has been shaping the future of nuclear energy in the U.S. for more than 60 years. Framatome Inc. is headquartered in Lynchburg, Virginia, with more than 2,400\+ employees across North America supporting nearly every nuclear plant in the nation.
From our facilities in Virginia, Washington, North Carolina, Pennsylvania, Massachusetts and beyond, we deliver outage services, advanced nuclear fuel technologies, and next\-gen reactor innovations that keep the lights on.
At Framatome, you will join a global team committed to creating reliable, carbon\-free energy. Here, you can:* Solve complex nuclear challenges that directly impact the climate.
- Build your career through technical fellowships, leadership roles, and global opportunities.
- Do your best work in a culture that values safety, innovation, and well\-being.
Framatome is an Equal Opportunity / Affirmative Action Employer. All qualified applicants receive consideration without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or veteran status.
Offers are contingent on background check and drug screen (where applicable). Positions may involve access to information subject to U.S. export control regulations; applicants must meet eligibility requirements (see DOE 810 Appendix A).
Salary Context
This $99K-$134K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $184K across 1486 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 2,799 AI roles we're tracking, AI/ML Engineer positions make up 71% of the market. At Framatome North America, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $175,000 based on 11,128 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $159,385. This role's midpoint ($116K) sits 33% below the category median. Disclosed range: $99K to $134K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $252,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,760; Mid: $159,385; Senior: $227,500; Director: $242,000; VP: $250,000.
Framatome North America AI Hiring
Framatome North America has 4 open AI roles right now. They're hiring across AI/ML Engineer. Based in Lynchburg, VA, US. Compensation range: $100K - $134K.
Location Context
Across all AI roles, 16% (460 positions) offer remote work, while 2,318 require on-site attendance. Top AI hiring metros: New York (2,241 roles, $208,300 median); San Francisco (1,822 roles, $252,000 median); Los Angeles (1,611 roles, $188,900 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 2,799 open positions tracked in our dataset. By seniority: 98 entry-level, 1,283 mid-level, 1,092 senior, and 326 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (460 positions). The remaining 2,318 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $252,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 30 roles); AI Safety ($274,200 median, 43 roles); Research Engineer ($260,000 median, 387 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 2,799 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (1,978), AI Software Engineer (197), Data Scientist (195). 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 (98) are outnumbered by mid-level (1,283) and senior (1,092) 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 326 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (460 positions), with 2,318 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 $200,000. Top-quartile roles start at $252,000, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,433 postings), Aws (840 postings), Rag (663 postings), Azure (639 postings), Gcp (537 postings), Pytorch (445 postings), Prompt Engineering (418 postings), Claude (396 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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