Interested in this AI/ML Engineer role at Razer?
Apply Now →Skills & Technologies
About This Role
Joining Razer will place you on a global mission to revolutionize the way the world games. Razer is a place to do great work, offering you the opportunity to make an impact globally while working across a global team located across 5 continents. Razer is also a great place to work, providing you the unique, gamer\-centric \#LifeAtRazer experience that will put you in an accelerated growth, both personally and professionally.
Job Responsibilities :
--------------------------
The Director, Product Marketing (AI Systems and Accessories) sets the global product marketing vision and strategy for a portfolio spanning consumer and edge hardware — AI laptops, desktops, and workstations, along with the docks, cooling solutions, and accessories that complete them. This leader owns category\-level positioning, go\-to\-market strategy, and commercial impact across multiple product lines, including translating differentiated silicon capabilities such as on\-device NPU performance into clear customer value for both mainstream and professional buyers. The role leads and develops a team of product marketing managers and partners closely with Product, Sales, Regional Marketing, and Executive Leadership to drive sustained growth, market leadership, and brand relevance in a rapidly evolving AI hardware category.
Essential Duties \& Responsibilities
- Own the global product marketing strategy for AI Systems and Accessories, defining long\-term positioning, portfolio architecture, and go\-to\-market frameworks across multiple product lines.
- Lead and manage a team of product marketing managers, providing clear direction, coaching, performance management, and career development to build a high\-performing, scalable organization.
- Set strategic direction for product launches at a portfolio level, ensuring cohesive messaging, differentiated positioning, and consistent execution across regions and channels.
- Partner with Product Management to influence roadmap decisions, pricing strategy, and lifecycle planning based on market insights, competitive intelligence, and customer needs across consumer and professional segments.
- Define the systems\-and\-accessories attach strategy, ensuring docks, cooling, and peripheral products are positioned to extend the value of the core system and drive incremental revenue.
- Translate technical capability — including on\-device AI performance, NPU\-accelerated workflows, and thermal and power design — into clear, credible, benefit\-led customer messaging, working with engineering to ensure claims are substantiated.
- Own category\-level business performance, including revenue impact, market share growth, and return on marketing investment, with accountability for key OKRs.
- Develop and oversee annual marketing plans and budgets for the AI systems and accessories portfolio, ensuring resources are allocated against the highest\-impact initiatives.
- Act as the primary product marketing voice to senior leadership and executive stakeholders, translating market complexity into clear strategic recommendations.
- Drive competitive and market intelligence programs, identifying emerging trends, white spaces, and threats, and ensuring insights are embedded into strategic planning.
- Champion thought leadership and brand storytelling that establishes authority and relevance in the AI hardware category, spanning creator, professional, and enthusiast audiences.
- Establish and optimize product marketing processes, tools, and best practices to improve efficiency, consistency, and cross\-functional alignment at scale.
- Partner with silicon, platform, and ISV partners on joint positioning, co\-marketing programs, and ecosystem storytelling.
- Support key customer, partner, and industry engagements, including major trade shows, press briefings, and strategic sales moments.
- Collaborate closely with Global and Regional Marketing and sales teams to ensure global strategy is effectively localized without diluting core positioning.
- Other duties as assigned.
Pre\-Requisites :
---------------------
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.
- Extensive experience in product marketing spanning both consumer and professional audiences, preferably within PCs, consumer electronics, computing hardware, or adjacent categories.
- Demonstrated success leading multi\-product, global go\-to\-market strategies with measurable business impact.
- Proven people leader with experience managing and scaling high\-performing teams.
- Working fluency in AI computing concepts — on\-device versus cloud inference, NPU/GPU/CPU tradeoffs, and common AI workloads — with the ability to translate technical capability into customer\-facing value.
- Strong strategic, analytical, and financial acumen, with the ability to connect marketing strategy to business outcomes.
- Exceptional executive\-level communication and presentation skills.
- Deep understanding of digital marketing channels, lifecycle marketing, and modern GTM frameworks.
- Experience marketing hardware through both direct and retail/channel routes to market.
- Experience operating in complex, matrixed organizations with global stakeholders.
- AI\-related certifications or formal coursework — such as NVIDIA Deep Learning Institute, Microsoft Azure AI, AWS Machine Learning, or Google Cloud AI credentials — are a plus.
Education \& Experience
- Bachelor's degree in Marketing, Business, Engineering, Computer Science, or a related field, or equivalent practical experience; MBA or advanced technical degree preferred.
- 10\+ years of product marketing experience, including 4\+ years leading and developing teams.
Salary Ranges:
$139,620\.00 \- $232,700\.00 (per annum)
Disclaimer: Exact compensation may vary based on skills, experience, and location.
Razer is proud to be an Equal Opportunity Employer. We believe that diverse teams drive better ideas, better products, and a stronger culture. We are committed to providing an inclusive, respectful, and fair workplace for every employee across all the countries we operate in. We do not discriminate on the basis of race, ethnicity, colour, nationality, ancestry, religion, age, sex, sexual orientation, gender identity or expression, disability, marital status, or any other characteristic protected under local laws. Where needed, we provide reasonable accommodations \- including for disability or religious practices \- to ensure every team member can perform and contribute at their best.
Are you game?
Salary Context
This $139K-$232K 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 Razer, 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. Director-level AI roles across all categories have a median of $274,554. This role's midpoint ($186K) sits 13% below the category median. Disclosed range: $139K to $232K.
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.
Razer AI Hiring
Razer has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Irvine, CA, US. Compensation range: $157K - $232K.
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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.