Interested in this AI/ML Engineer role at Keysight Technologies?
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Overview:
Keysight is at the forefront of technology innovation, delivering breakthroughs and trusted insights in electronic design, simulation, prototyping, test, manufacturing, and optimization. Our ~16,800 employees create world-class solutions in communications, 5G, automotive, energy, quantum, aerospace, defense, and semiconductor markets for customers in over 100 countries. Learn more about what we do.
Our award-winning culture embraces a bold vision of where technology can take us and a passion for tackling challenging problems with industry-first solutions. We believe that when people feel a sense of belonging, they can be more creative, innovative, and thrive at all points in their careers. Responsibilities:
Keysight is seeking a Senior Program Manager, NPI Solutions to incubate new product ideas within our AI Infrastructure test portfolio. This role owns the earliest, highest-ambiguity phase of new product introduction — from shaping a compelling technical vision and business case, through building and leading a small team to deliver a working MVP that proves out technical and market viability. The ideal candidate pairs deep technical credibility in networking (Ethernet and/or RDMA) with extensive customer-facing experience, and thrives on driving a shared vision across engineering, product, and business stakeholders in the face of ambiguity.
Responsibilities
- Identify and incubate new product opportunities in the AI Infrastructure test space, carrying ideas from early concept through a proven MVP.
- Define the technical vision, product requirements, and business case for new offerings; present the case to senior stakeholders to secure investment decisions.
- Assemble, lead, and coordinate a small cross-functional team (engineering, test, and product) to design, build, and validate each MVP.
- Engage directly with customers and industry partners to validate problem statements, gather requirements, and pressure-test assumptions.
- Drive alignment across multiple stakeholders — engineering, product management, sales, and executive leadership — despite ambiguity and evolving requirements.
- Define success criteria and metrics for MVP validation, and make go/no-go recommendations on scaling incubated ideas into funded programs.
- Track and communicate program status, risks, and key decisions to leadership on a regular cadence.
- Stay current on Ethernet and RDMA networking trends, AI data center infrastructure, and the competitive landscape to inform incubation priorities.
- Represent Keysight at customer briefings, industry events, and standards discussions as needed (up to ~25% travel).
Qualifications: Required:
- Bachelor's degree in Computer Science, Electrical Engineering, or a related field.
- 10+ years of experience in engineering or product/program management, including building and defining products from concept through launch.
- Strong technical background in networking, with depth in Ethernet and/or RDMA technologies.
- Significant experience in customer-facing roles (e.g., pre-sales, solutions engineering, technical account management, or field applications).
- Demonstrated ability to lead a small team through ambiguity, from early-stage concept to a working MVP.
- Proven ability to build and drive a shared vision across multiple stakeholders and functions, often without direct authority.
- Excellent written and verbal communication skills; able to translate technical detail for both engineering and executive audiences.
Preferred:
- Experience in AI infrastructure, data center networking, or the network test and measurement industry.
- Master's degree or MBA.
- Familiarity with lean startup / incubation methodologies (MVP definition, hypothesis-driven validation).
- Track record of presenting to and influencing senior executive stakeholders.
#LI-MO1 Visa sponsorship is not available for this position.
Calabasas, CA Pay Range: MIN $167.00 - MAX $279,000.00 Santa Clara, CA Pay Range: MIN $184,000.00 - MAX $306,000.00 This position requires access to certain goods, software, technology, or technical data subject to U.S. export control laws and regulations. Under these laws and regulations, U.S. persons (which includes U.S. citizens, U.S. nationals, lawful permanent residents, refugees, and asylees) working for Keysight can access export-controlled items without authorization from the U.S. government. For any individual who is not a U.S. person, Keysight may need authorization from the U.S. Department of State, U.S. Department of Commerce, or other appropriate federal agency before the individual can access export-controlled items. Employment in this position for non-U.S. persons is contingent on Keysight’s ability to obtain any required government authorizations.
Most offers will be between the minimum and the midpoint of the Salary Range listed above. Note: For other locations, pay ranges will vary by region
US Employees may be eligible for the following benefits:* Medical, dental and vision
- Health Savings Account
- Health Care and Dependent Care Flexible Spending Accounts
- Life, Accident, Disability insurance
- Business Travel Accident and Business Travel Health
- 401(k) Plan
- Flexible Time Off, Paid Holidays
- Paid Family Leave
- Discounts, Perks
- Tuition Reimbursement
- Adoption Assistance
- ESPP (Employee Stock Purchase Plan)
Careers Privacy StatementKeysight is an Equal Opportunity Employer.
Salary Context
This $0K-$306K range is below the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Keysight 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 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 $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($153K) sits 28% below the category median. Disclosed range: $167 to $306K.
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.
Keysight Technologies AI Hiring
Keysight Technologies has 2 open AI roles right now. They're hiring across AI Architect, AI/ML Engineer. Positions span Loveland, CO, US, Calabasas, CA, US. Compensation range: $306K - $326K.
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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