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About This Role
Amphenol Communications Solutions (ACS), a division of Amphenol Corporation, is a world leader in interconnect solutions for Communications, Mobile, RF, Optics, and Commercial electronics markets. Amphenol Corporation is one of the world’s largest designers and manufacturers of electrical, electronic and fiber optic connectors and interconnect systems, antennas, sensors and sensor-based products and coaxial and high-speed specialty cable. ACS has an expansive global presence in research and development, manufacturing, and sales. We design and manufacture a wide range of innovative connectors as well as cable assemblies for diverse applications including server, storage, data center, mobile, RF, networking, industrial, business equipment, and automotive.
Position: AI Engineer – High Speed Backplane Products
Location: Nashua, NH
Amphenol High Speed Products Group is a global leader in high-speed, high-bandwidth interconnect solutions supporting the world’s largest technology companies across AI/ML infrastructure, telecom, datacom, storage, networking, servers, and next-generation computing platforms. Our products are critical enablers for Tier 1 OEMs and hyperscale customers worldwide. With engineering, manufacturing, and commercial operations across the globe, we are seeking a highly motivated AI Engineer to add to our product development and customer support capabilities.
Responsibilities:
As an AI Engineer within the High Speed Backplane Products Business Unit, you will partner with various engineering leaders and functional teams to identify, develop, and implement Artificial Intelligence solutions that improve engineering efficiency, enhance decision-making, and increase customer satisfaction.
- Identify opportunities to leverage AI technologies to improve engineering processes, productivity, and operational effectiveness.
- Collaborate with business stakeholders to understand workflows, challenges, and opportunities for automation and optimization.
- Develop, test, and implement AI-driven solutions, including design and simulation tools, reporting and analysis tools, dashboards, and customer-use tools.
- Partner with engineering teams to turn ideas into functional tools.
- Train employees and teams on AI tools, best practices, and responsible AI usage.
- Create user guides, training materials, and documentation to support AI adoption and ongoing learning.
- Serve as a subject matter expert on emerging AI technologies, trends, and applications relevant to the business.
- Partner with IT, business leaders, and cross-functional teams to ensure AI solutions align with company objectives, security requirements, and governance standards.
- Recommend and implement process improvements that streamline workflows, reduce manual effort, and increase accuracy.
- Maintain awareness of AI compliance, privacy, and ethical considerations in business applications.
Qualifications:
- Bachelor's degree in Data Analytics, Information Systems, Computer Science, Engineering, or a related field.
- 3-5+ years of experience with Python required. Additional experience with C#, C++, MATLAB, or similar prefrerred.
- Experience integrating software with third-party engineering applications through APIs, scripting interfaces, etc
- Experience automating, or integrating, engineering applications such as ANSYS, HFSS, CST, MATLAB, Creo, SolidWorks, or similar platforms
- Familiarity with AI platforms, generative AI tools, automation technologies, and data visualization software.
- Strong analytical and problem-solving skills with the ability to translate business needs into technical solutions.
- Experience developing reports, dashboards, and performance metrics.
- Excellent communication and presentation skills, including the ability to train and influence users at all organizational levels.
- Strong project management and organizational skills.
- Ability to work independently and manage multiple initiatives simultaneously. You will be at the forefront of AI/ML technology, working on programs that power the next generation of global infrastructure.
Amphenol Corporation is proud of our reputation as an excellent employer. Our focus is to provide the highest level of support and responsiveness to both our employees and our customers, the world's largest technology companies. Amphenol Corporation offers the opportunity for career growth within a global organization. We believe that Amphenol Corporation is unique in that every employee, regardless of his or her position, has the ability to positively impact the business.
Amphenol is an “Equal Opportunity Employer” - Minority/Female/Disabled/Veteran/Sexual Orientation/Gender Identity/National Origin. For additional company information please visit our website at https://www.amphenol-cs.com/
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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Amphenol, 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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Amphenol AI Hiring
Amphenol has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Nashua, NH, US, US. Compensation range: $150K - $150K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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