Interested in this AI/ML Engineer role at Edmund Optics?
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Overview:
Edmund Optics is seeking a Senior Director of Artificial Intelligence to lead the strategy, development, and deployment of AI initiatives across the organization. This role will define the enterprise AI roadmap, and build and mentor a high\-performing team. This role will partner with the continuous improvement team to determine areas within the organization that can benefit the most from AI, as well as working with the analytics team on the development and rollout of AI\-driven solutions for Edmund Optics. This role will partner with engineering, manufacturing, sales, and IT leadership to embed AI into core business processes. The ideal candidate combines deep technical expertise with strong business acumen and the ability to translate AI capabilities into measurable value for a global optics and photonics manufacturer.
Responsibilities:
- Strategy \& Vision: Define and drive the company\-wide AI strategy, identifying high\-impact opportunities across manufacturing, HR, quality control, supply chain, R\&D, sales, and customer service.
- Leadership \& Team Building: Recruit, develop, and lead a team of new to industry AI designers and project managers to implement major AI projects. Work with the analytics team to establish best practices for model development, deployment, and governance. Move towards a hub\-and\-spoke operating model, with a centralized team of specialists defining standards and practices while working closely with embedded teams across the business.
- Cross\-Functional Partnership: Collaborate with continuous improvement and with major functional areas such as optical engineering, manufacturing operations, IT, and commercial teams to identify use cases for advancement (e.g., computer vision for defect detection, predictive maintenance, generative AI for design/quoting tools, customer\-facing chat/support tools).
- Solution Delivery: Be accountable for the full lifecycle of AI projects — from proof of concept through production deployment and monitoring — ensuring scalability, reliability, and ROI.
- Data Infrastructure: Partner with IT, analytics, and cybersecurity to ensure the organization has the data pipelines, infrastructure, and governance needed to support AI initiatives.
- Innovation \& Emerging Tech: Stay current on advances in AI (including large language models, computer vision, generative AI, agentic AI, and industrial AI applications) and assess applicability to optics manufacturing and photonics R\&D.
- Responsible AI \& Governance: Establish policies for data privacy, model risk, security, and ethical use of AI across the enterprise.
- Executive Communication: Report to senior leadership on AI initiative progress, business impact, budget, and risk; build the business case for continued AI investment.
- Vendor \& Partnership Management: Evaluate and manage relationships with AI technology vendors, cloud providers, and external research partners.
Qualifications:
To perform this position successfully, an individual must be able to perform each essential function 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.
Required Skills \& Abilities* Proven track record of deploying AI and/or ML solutions at scale in a manufacturing, industrial, or engineering\-driven environment.
- Strong knowledge of machine learning, computer vision, agentic AI, and/or generative AI techniques and their practical business applications.
- Strong change management skills, especially implementing AI solutions in major functional areas of a large organization.
- Experience building and leading technical teams.
- Excellent communication skills with the ability to translate technical concepts for non\-technical executive stakeholders.
- Experience managing budgets and demonstrating ROI on technology investments
- Comply with federal, state, and company policies, procedures, and regulations
Education/Experience* Bachelor's degree in Computer Science, Data Science, Engineering, or related field (Master's or PhD preferred).
- 5\+ years of experience in AI, ML, data science, or related technical fields, with 5\+ years in a leadership role adjacent to AI (computer science, engineering, data analytics or other related fields).
The following qualifications are preferred:* Experience in optics, photonics, precision manufacturing, or a related technical industry.
- Experience with cloud AI or ML platforms, especially agentic AI tools.
- Experience with AI data platforms, especially Snowflake.
Physical Requirements
Ability to operate office equipment such as a copier; ability to see details at a close range; ability to sit at desk or PC for long periods of time; work in office setting.
All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran. *Think you meet some of the requirements but not all? Studies have shown that women and people of color are less likely to apply to jobs for which they do not meet every qualification. If you see a role that interests you, we encourage you to apply, regardless of whether or not your experience is completely aligned with the job description. Edmund Optics is committed to becoming the most diverse, equitable, and inclusive workplace within the Optics and Photonics Industry and beyond. You may be a great candidate for this role or others within Edmund Optics.* Compensation Range Transparency:
At Edmund Optics, we are committed to transparency and equity in our hiring practices. The posted salary range for this role reflects the expected base pay. The actual offer will be based on multiple factors, including but not limited to relevant skills, education, work experience, business needs, and geographic location. Salary Range:
$190,000 \- $235,000 per year Benefits:* Medical, Dental, and Vision Insurance
- Life, AD\&D, Short and Long\-Term Disability Insurance
- Generous Paid Time Off (PTO)
- Tuition Reimbursement
- 401(k) Retirement Plan with Company Match up to 3%
- Daycare and Gym Reimbursement
- Paid Parental Leave and New Mother Benefits
- Training and Development Opportunities
*Availability of these benefits may depend on the country and employment type.*
\#LI\-ONSITE
Salary Context
This $190K-$235K range is above the median 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 Edmund Optics, 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 in Demand for This Role
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. Director-level AI roles across all categories have a median of $274,554. Disclosed range: $190K to $235K.
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.
Edmund Optics AI Hiring
Edmund Optics has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Barrington, NJ, US. Compensation range: $235K - $235K.
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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