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About This Role
As a global leader in radiation safety, detection and measurement, Mirion Technologies is advancing radiation safety through innovative technologies and solutions.
Mirion's Nuclear & Safety business group builds the hardware, software, and services that keep nuclear power plants, laboratories and research facilities, defense operations, and radiation-hazard workplaces safe and effective. We're investing in a small, high-impact team of embedded AI Solutions Engineers who will sit inside our Nuclear & Safety business units and turn everyday operational friction across manufacturing, quality, service, and back-office workflows, into measurable efficiency gains using modern AI.
This is not a research role and not a central-platform role. You will spend most of your time with the people who run the business (i.e. plant managers, quality engineers, field service leaders, product managers), finding the highest-leverage problems and then personally designing, building, and shipping the solution. Your toolkit spans the full range of applied AI: computer vision for visual inspection and image-based QA, spectral and sensor-data analytics, basic software development, and agentic AI / LLM-based automation for decision support and workflow orchestration. You'll report directly to the Chief AI and Digital Officer, giving you a direct line to enterprise AI strategy while you operate with the autonomy of a startup engineer inside a public, nuclear-regulated industrial company.
What You'll Do
Find and frame the opportunity
- Embed within the Nuclear & Safety business unit, attending their staff meetings, walking their production floor, shadowing their field service and quality teams to fully understand the business and landscape of problems
- Build a first-hand map of where manual, error-prone, or slow processes are costing time and money.
- Translate ambiguous, non-technical pain points ("our detector inspection and QA review takes too long," "we can't keep up with service tickets") into scoped, buildable AI solutions, and build the business case for prioritizing them.
Build and ship
- Design and deploy solutions for use cases such as automated visual inspection of detectors and assemblies, defect and anomaly detection, spectral and sensor-data analysis, and image-based quality verification.
- Design and deploy agentic AI systems (LLM-based agents, workflow orchestration, and automation) to reduce manual effort in manufacturing, service, quality and compliance documentation, and other back-office functions.
- Own the full lifecycle: rapid prototyping, pilot with real users, hardening for production, and handoff or ongoing support, leveraging shared enterprise services and without waiting for a large team to do it for you.
Communicate and drive adoption
- Present progress, trade-offs, and results in plain business language to business counterparts (plant managers, quality leaders, and executives who don't have an AI background) and adjust your approach based on their feedback.
- Define and track the metrics (cycle time, error rate, cost, throughput) that prove a solution is actually working, and course-correct quickly when it isn't.
- Lead change management and light training so solutions get adopted, not shelved.
Operate within the broader AI function
- Partner with the Chief AI & Digital Officer and central AI/Digital team to stay aligned on architecture, security, data governance, and tooling standards, and to bring reusable patterns back to the center.
- Apply appropriate rigor for a regulated nuclear environment (nuclear quality assurance, validation, documentation, export controls, cybersecurity) in partnership with Quality, Regulatory, and Compliance colleagues.
What Success Looks Like in Year One
- At least 2–3 AI-driven solutions in production, each tied to a measurable operational KPI (time saved, defect reduction, cost avoidance, throughput gained).
- A trusted, self-sustaining relationship with business unit leadership. They should come to you with problems rather than reaching out to the CAIDO, directly.
- A reusable playbook or pattern (for computer vision, for agentic workflows, or both) that can be extended to other Nuclear & Safety business units or replicated by peer AI solutions engineers.
What You Bring
Required
- 5+ years shipping production software or applied ML/AI systems, with real users depending on what you built.
- Experience in software engineering and cloud-based application development and deployment
- Hands-on experience building agentic or LLM-based systems (e.g., LangChain, LangGraph, AutoGen, or a comparable custom agent framework), including prompt design and retrieval-augmented approaches.
- Strong core software engineering: Python, cloud platforms (Azure, AWS, or GCP), APIs, and enough MLOps/DevOps fluency to get something into production and keep it running.
- A demonstrated track record of high agency. Ideally a history of projects you identified, scoped, and delivered with minimal direction, not just tickets you executed that were assigned in JIRA.
- Excellent communication skills with non-technical audiences. Strong ability to clearly explain what a model does and doesn't do without jargon, and knowledge of how to build trust with non-technical operators.
- Comfort with ambiguity and autonomy. You will often be the only AI/software person in the room and working daily with those with little-to-no AI skills. You will be part of a team of centralized peers with strong skills but will operate independently to deliver business-unit-specific results.
Strongly Preferred
- Hands-on experience building computer vision solutions (e.g., OpenCV, PyTorch or TensorFlow, image classification/detection/segmentation pipelines), or ML on spectra, waveforms, and sensor time-series data.
- Experience in Azure or AWS, Databricks, and Claude/OpenAI integration via AWS Bedrock or Azure Foundry
- Experience in a regulated industry — nuclear power, defense, medical devices, or industrial manufacturing under a formal quality system (e.g., ISO 9001, 10 CFR 50 Appendix B, NQA-1).
- Prior experience as an embedded, business-facing, or "forward-deployed" engineer, solutions engineer, or applied AI generalist, rather than a purely research-facing ML role.
- Domain exposure to nuclear power plant instrumentation and controls, radiation detection and measurement, gamma spectroscopy, health physics, or radiation safety and monitoring workflows.
Why This Role
- Direct visibility with the Chief AI & Digital Officer and a real hand in shaping how AI gets built and scaled across Mirion Nuclear & Safety.
- The rare combination of startup-style autonomy and ownership with the stability, resources, and mission of a global public company protecting workers, communities, and the environment.
- A builder's role. You will ship things that change how people work, not produce slide decks about how AI might someday help.
Mirion Technologies (NYSE: MIR) is a global leader in radiation safety, science, and medicine, protecting people and the planet from the harmful effects of ionizing radiation. Mirion Nuclear & Safety powers Nuclear Power Plants, Laboratory & Research (including spectroscopy and scientific analysis), Radiation Safety & Monitoring, and Defense.
Mirion offers a comprehensive benefits package that includes Vacation & PTO Time, medical, dental, and vision coverage, a 401(k) retirement savings plan with matching, HSA and FSA options, life and disability insurance, an Employee Assistance Program, wellness resources, and several voluntary benefits including legal assistance, identity protection, and pet insurance.
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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Mirion, 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 $212,040 based on 13,780 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Mirion AI Hiring
Mirion has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Meriden, CT, US, Remote, US.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 roles, $210,000 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, 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 $266,050 median, while Prompt Engineer roles sit at $140,000. 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 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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