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About This Role
At BWX Technologies, Inc. (NYSE: BWXT), we are People Strong, Innovation Driven. A U.S.\-based company, BWXT is a Fortune 1000 and Defense News Top 100 manufacturing and engineering innovator that provides safe and effective nuclear solutions for global security, clean energy, environmental restoration, nuclear medicine and space exploration. With more than 7,800 employees, BWXT has 14 major operating sites in the U.S., Canada and the U.K. We are the sole manufacturer of naval nuclear reactors for U.S. submarines and aircraft carriers. Our company supplies precision manufactured components, services and fuel for the commercial nuclear power industry across four continents. Our joint ventures provide environmental restoration and operations management at a dozen U.S. Department of Energy and NASA facilities. BWXT’s technology is driving advances in medical radioisotope production in North America and microreactors for various defense and space applications. For more information, visit www.bwxt.com. Follow us on LinkedIn, X, Facebook and Instagram.
Welcome to BWXT
We believe in empowering our employees and cultivating a dynamic workplace that fosters growth and collaboration. Whether you’re an experienced professional or just starting your career, you'll find opportunities to challenge yourself, learn from seasoned experts, and contribute to nuclear innovation. We require a diverse range of skills to support our work in the markets that drive our business and welcome candidates from all backgrounds and life stages who are passionate about making a difference.
Position Overview:
The Manufacturing AI \& Analytics Architect will guide the design and delivery of AI, advanced analytics, data architecture, and decision\-support solutions for BWXT production manufacturing environments. This role will partner with manufacturing, engineering, quality, maintenance, operations, supply chain, cybersecurity, and IT teams to translate operational challenges into secure, scalable, supportable solutions that improve quality, throughput, downtime, process visibility, and data\-driven decision making.
Location:
Onsite in Melbourne, FL or Lynchburg, VA. Fully remote option available.
Your Day to Day as a Manufacturing AI \& Analytics Architect:
- Partner with manufacturing and business\-unit leaders to identify, prioritize, and define high\-value AI and analytics use cases across production, quality, maintenance, supply chain, safety, and operational performance.
- Translate manufacturing workflows and operational pain points into technical requirements, data requirements, solution architectures, implementation plans, and measurable success criteria.
- Design practical solutions such as dashboards, alerts, predictive models, anomaly detection, optimization tools, AI assistants, data products, APIs, workflow automation, and lightweight applications.
- Analyze data from ERP, MES, SCADA, historians, PLCs, sensors, quality systems, maintenance systems, production logs, engineering systems, spreadsheets, and other operational sources.
- Develop reusable data models, features, semantic definitions, KPIs, and architecture patterns that can scale across plants, lines, stations, processes, and business units.
- Partner with data engineering, software engineering, application, platform, and cybersecurity teams to move prototypes into production\-ready solutions.
- Support data governance, metadata, data quality, security, regulatory compliance, lifecycle management, and stewardship practices for manufacturing data and analytics.
- Apply MLOps and production support practices such as version control, testing, model deployment, monitoring, drift detection, retraining, logging, and documentation.
- Communicate technical concepts, assumptions, limitations, risks, and recommendations clearly to plant teams, engineers, leaders, and non\-technical stakeholders.
- Challenge assumptions constructively, clarifies ambiguous requirements, and helps prioritize solutions that provide practical business value.
Required Qualifications:
- Bachelor’s degree in Data Science, Computer Science, Information Systems, Statistics, Industrial Engineering, Manufacturing Engineering, Engineering, Operations Research, Applied Mathematics, or a related field.
- Minimum of 10 years of relevant experience, including some experience in data architecture, analytics, AI/ML, data engineering, manufacturing systems, and/or solution architecture.
- Experience designing or delivering analytics, AI/ML, data, BI, automation, or decision\-support solutions in a professional environment.
- Strong SQL skills and experience working with large, complex datasets.
- Python or similar analytical programming experience using tools such as pandas, NumPy, scikit\-learn, PyTorch, TensorFlow, XGBoost, statsmodels, or similar libraries.
- Experience with supervised and unsupervised analytics or machine learning methods, such as classification, regression, clustering, anomaly detection, time\-series analysis, forecasting, optimization, or statistical process analysis.
- Experience working with cloud\-based data and analytics platforms such as Azure, AWS, GCP, Databricks, Snowflake, Fabric, or similar environments.
- Strong understanding of data architecture, including data modeling, data integration, metadata, data quality, data governance, and data lifecycle management.
- Experience communicating technical findings in business\-friendly language and produce clear technical architecture and support documentation.
- Highly self\-motivated, collaborative, detail\-oriented, and results\-driven.
- Must be a U.S. citizen.
- Must be able to obtain and maintain a U.S. Department of Energy (DOE) clearance.
Preferred Additional Qualifications:
- Experience applying AI, analytics, data science, or data architecture in manufacturing, industrial, nuclear, aerospace, defense, semiconductor, quality, maintenance, supply chain, or operations environments.
- Experience with manufacturing systems such as MES, SCADA, PLCs, historians, CMMS/EAM, QMS, ERP, industrial IoT platforms, engineering systems, or production scheduling systems.
- Understanding of manufacturing KPIs such as throughput, cycle time, downtime, OEE, scrap, rework, takt time, bottlenecks, quality escapes, first\-pass yield, schedule adherence, and safety events.
- Experience with Azure, Azure AI/OpenAI, Databricks, Power BI, SQL Server, Python, APIs, Power Platform, containerized applications, or similar enterprise platforms.
- Experience with real\-time or near\-real\-time anomaly detection, streaming data, event\-driven architectures, MQTT, Kafka, Spark, Azure Event Hubs, Azure IoT, or similar technologies.
- Experience building dashboards, internal applications, or decision\-support tools using Power BI, Tableau, Grafana, Streamlit, Dash, FastAPI, or similar tools.
- Experience in regulated, high\-security, safety\-conscious, export\-controlled, or mission\-critical environments.
What We Offer:
- Competitive salary and benefits package, including health, dental, and retirement plans.
- Flexible work schedules and paid time off to promote a healthy work\-life balance.
- Professional development opportunities, including mentorship programs and sponsorship for continuing education.
- An inclusive atmosphere that celebrates new perspectives and supports collaboration between different generations.
- The chance to be part of a mission\-driven organization making a positive impact on the future of energy.
- Opportunities for continuous learning and training to grow throughout your career!
Pay: $86,450 \- $136,000
The base salary range for this position in Florida (US\-FL) at the start of employment is expected to be between $86,450 and $136,000 per year. However, the base salary offered is based on local job market factors, and may vary further depending on factors specific to the selected job candidate, such as job\-related knowledge, skills, experience, and other objective business considerations. Subject to these considerations, the total compensation package for this position may also include other elements, such as an annual cash incentive in addition to a full range of medical, retirement, and/or other benefits. Details of participation in these benefit plans will be provided at such time the selected job candidate receives an offer of employment. If hired, the selected job candidate will be employed 'at\-will,’ unless employed at a location and in a position subject to a collective bargaining agreement. The company further reserves the right to modify base salary (as well as any other discretionary payment, compensation or benefit program) at any time, including for reasons related to individual performance, company or individual department/team performance, and other market factors.
As a federal government contractor, BWX Technologies, Inc. and any subsidiaries, affiliates and related entities (“BWXT” or the “Company”) complies with all federal, state, and local laws and customer requirements regarding health and safety protocols. As such, all BWXT new hires will be required to adhere to applicable Company health and safety requirements within the workplace as a condition of employment.
All candidates must be U.S. citizens. Selected applicants are required to successfully complete a pre\-employment check and drug screening. In addition, the position may require the ability to obtain and maintain applicable federal eligibility requirements for access to classified/sensitive information or matter which involves an extensive criminal and financial background investigation, drug test, previous employment, and reference verifications.
BWXT is committed to the concept of Equal Employment Opportunity. We have established procedures to ensure that all personnel actions such as recruitment, compensation, career development, benefits, company\-sponsored training and social recreational programs are administered without regard to race, color, religion, sex, national origin, citizenship, age, disability, protected veteran or other protected status.
BWX Technologies, Inc. and its affiliates and subsidiaries (BWXT) is not responsible for and does not accept any liability for fees or other costs associated with resumes or candidates presented by recruiters or employment agencies, unless a binding, written recruitment agreement between BWXT and the recruiter or agency exists prior to the presentation of candidates or resumes to BWXT and includes the specific services, job openings, and fees to be paid (“Agreement”). BWXT may consider any candidate for whom a recruiter or agency has submitted an unsolicited resume and explicitly reserves the right to hire such candidate(s) without any financial obligation to the recruiter or agency unless an Agreement is in place prior to presentation and such Agreement explicitly encompasses the job opening for which such fees or costs are sought. An email, verbal or other informal contact with any person within BWXT will not create a binding agreement. Agencies or recruiters without an Agreement are directed not to contact any hiring managers of BWXT with recruiting inquiries or resumes. Recruiters and agencies interested in partnering with BWXT may contact BWXT’s Talent Acquisition team at talent\[email protected].
Salary Context
This $86K-$136K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At BWX Technologies, 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($111K) sits 48% below the category median. Disclosed range: $86K to $136K.
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 Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
BWX Technologies AI Hiring
BWX Technologies has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Melbourne, FL, US. Compensation range: $136K - $136K.
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 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 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).
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 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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