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About This Role
Be visionary
Teledyne Technologies Incorporated provides enabling technologies for industrial growth markets that require advanced technology and high reliability. These markets include aerospace and defense, factory automation, air and water quality environmental monitoring, electronics design and development, oceanographic research, deepwater oil and gas exploration and production, medical imaging and pharmaceutical research.
We are looking for individuals who thrive on making an impact and want the excitement of being on a team that wins.
Job Description
Summary
Coordinates and executes AI adoption activities across the Engineered Systems Segment (TBE, TAES, TESI) on behalf of the Director of AI Adoption \& Process Excellence. Plans and delivers hands\-on engagements and department\-level training to help everyday users apply approved AI tools to their daily work. Supports the AI Champions Network, tracks adoption activity, and assists with AI\-related projects tasked by segment leadership.
Works closely with the Director to translate AI adoption strategy into scheduled activities, training, and coordinated outreach across departments and business units. Builds relationships with employees at all levels to identify practical use cases and remove barriers to adoption. Prioritizes competing requests across the segment and communicates progress to the Director. Tasks and assignments are often non\-routine and require judgment in applying approved AI tools to varied, real\-world processes. Receives assignments in the form of objectives and goals from the Director and determines the method for achieving them. Duties are performed under limited supervision.
Essential Duties and Responsibilities include the following. (other duties may be assigned):
- Serves as the primary point of coordination for the Director's AI adoption initiatives, translating strategic direction into scheduled activities, deliverables, and follow\-through across the Engineered Systems Segment (TBE, TAES, TESI).
- Supports execution of Director\-assigned AI projects and initiatives for segment leadership, including data pulls, analysis, drafts, and presentation materials.
- Plans, schedules, and facilitates recurring training and coaching engagements, providing hands\-on demonstrations and troubleshooting to help employees apply approved AI tools (e.g., Microsoft Copilot, Claude) to their daily work.
- Stays current on new and updated AI tools, features, and industry practices, and shares relevant developments with the Director, users across the Segment and the AI Champions Network.
- Supports and coordinates activities of the AI Champions Network across the segment, helping champions share best practices, track adoption metrics, and escalate barriers to the Director.
- Conducts one\-on\-one and small\-group engagements across departments and business units to identify practical AI use cases and coach everyday users toward more efficient, AI\-assisted workflows.
- Assists in developing training materials, quick\-reference guides, and how\-to documentation for approved AI tools and emerging capabilities.
- Monitors and reports on AI adoption activity and trends across email, calendar, Teams, and SharePoint sources to support recurring enterprise reporting.
- Assists in evaluating new AI tools, use cases, and pilot programs, and documents findings for leadership review.
- Coordinates with IT, Legal, and Contracts to help ensure AI tool usage aligns with corporate AI policy (e.g., LGL\-05\) and any applicable customer flow\-down requirements.
- Builds simple demonstrations, templates, or lightweight automation to demonstrate practical AI applications to non\-technical audiences.
- Prepares presentation materials and status updates summarizing AI adoption progress for department, segment, and executive audiences.
- Maintains organized records of AI\-related engagements, requests, and outcomes to support continuity and knowledge sharing.
- Performs other duties as assigned to support the segment's AI adoption and process improvement goals.
Supervisory Responsibilities
This position does not have direct supervisory responsibility over other employees. It does provide functional coordination and guidance to AI Champions across departments and business units of the segment, including assigning tasks, checking progress, and helping resolve adoption barriers. Responsibilities may expand to include direct reports as the AI adoption function grows.
Competencies To perform the job successfully, an individual should demonstrate the following competencies:
- Analytical \- Collects and researches data; uses intuition and experience to complement data.
- Problem Solving \- Identifies and resolves problems in a timely manner; gathers and analyzes information skillfully; works well in group problem solving situations.
- Project Management – Communicates changes and progress.
- Technical Skills \- Pursues training and development opportunities; strives to continuously build knowledge and skills.
- Oral Communication \- Speaks clearly and persuasively in positive or negative situations; listens and gets clarification; responds well to questions; participates in, facilitates or leads meetings.
- Written Communication \- Writes clearly and informatively; Edits work for spelling and grammar; Varies writing style to meet needs; Presents numerical data effectively; Able to read and interpret written information.
- Teamwork – Gives and welcomes feedback; supports everyone’s efforts to succeed.
- Leadership/ Managing People \- Exhibits confidence in self and others; Inspires and motivates others to perform well; Effectively influences actions and opinions of others; Accepts feedback from others; Gives appropriate recognition to others. Comes to the forefront in case of crisis, and is able to think and act in creative ways in difficult situations. Holds team accountable. Ensures work responsibilities are covered when absent. Takes responsibility for subordinates' activities; Makes him/herself available to staff; Provides regular performance feedback; Develops subordinates' skills and encourages growth; Fosters quality focus in others; Improves processes, products and services. Continually works to improve supervisory skills.
- Business Acumen \- Understands business implications of decisions.
- Ethics \- Treats people with respect; works with integrity and ethically; upholds organizational values.
- Organizational Support \- Follows policies and procedures; supports organization’s goals and values.
- Judgment \- Displays willingness to make decisions; Exhibits sound and accurate judgment; Supports and explains reasoning for decisions; Includes appropriate people in decision\-making process; Makes timely decisions.
- Motivation – Demonstrates persistence and overcomes obstacles.
- Planning/Organizing – Prioritizes and plans work activities; uses time efficiently.
- Professionalism – Reacts well under pressure; treats others with respect and consideration regardless of their status or position; accepts responsibility for own actions.
- Quality \- Demonstrates accuracy and thoroughness; looks for ways to improve and promote quality; applies feedback to improve performance; monitors own work to ensure quality.
- Quantity – Completes work in a timely manner; strives to increase productivity.
- Safety and Security – Observes safety and security procedures including using Personal Protective Equipment (PPE) as required and wearing company issued badge when on company property; Reports potentially unsafe conditions; Uses equipment and material properly.
- Adaptability \- Adapts to changes in the work environment; able to deal with frequent change, delays, or unexpected events.
- Initiative – Seeks increased responsibilities; asks for and offers help when needed.
- Innovation – Generates suggestions for improving work.
Qualifications
To perform this job successfully, an individual must be able to perform each essential duty satisfactorily. The requirements listed below are representative of the knowledge, skill, and/or ability required. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions
Education and/or Experience:
Bachelor's degree (B.A. or B.S.) from a four\-year college or university in any field, and 5\-10 years of related work experience with hands\-on, practical application of AI tools to real\-world business processes; or equivalent combination of education and experience. An advanced degree is not required.
Language Skills:
- Ability to read, analyze and interpret various business documents, technical procedures and government regulations.
- Ability to write reports and correspondence.
- Ability to prepare and effectively present information and response to questions before groups of customers or employees of organization.
Mathematical Skills:
- Ability to add, subtract, multiply, and divide in all units of measure, using whole numbers, common fractions, and decimals.
- Ability to compute rate, ratio, and percent and to draw and interpret bar graphs. Ability to apply concepts of basic algebra and geometry.
Reasoning Ability:
- Ability to solve practical problems and deal with a variety of concrete variables in situations where only limited standardization exists.
- Ability to interpret a variety of instructions furnished in written, oral, diagram, or schedule form.
Computer Skills:
- Must be very comfortable with computers and highly proficient across the full Microsoft Office/M365 suite (Word, Excel, PowerPoint, Outlook, Teams, SharePoint).
- Hands\-on, working experience with generative AI tools (e.g., Microsoft Copilot, Claude, ChatGPT, or similar) and the ability to quickly learn and adopt new software and AI platforms is required.
Other Essential Duties
- Follows all import/export requirements, consulting with facility import/export personnel as required.
Other Skills and Abilities
- Hands\-on experience with a range of AI tool categories (e.g., chat\-based assistants, agentic/workflow tools, AI\-enabled Microsoft 365 features) applied to real\-world business processes (required).
- Knowledge of ISO and/or AS9100 (a plus, not required).
- Familiarity with data analysis, reporting, or low\-code/no\-code automation tools (e.g., Power BI, Power Automate) is a plus.
Other Qualifications
- Ability to travel (domestically) approximately 5\-10%.
- Active Government Security Clearance
- US Citizenship with ability to attain/maintain government security clearance.
\#TBE
Teledyne and all of our employees are committed to conducting business with the highest ethical standards. We require all employees to comply with all applicable laws, regulations, rules and regulatory orders. Our reputation for honesty, integrity and high ethics is as important to us as our reputation for making innovative sensing solutions.
Teledyne is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability or veteran status, age, or any other characteristic or non\-merit based factor made unlawful by federal, state, or local laws.
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 Teledyne FLIR, 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.
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.
Teledyne FLIR AI Hiring
Teledyne FLIR has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Huntsville, AL, US.
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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