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Description:
Ready to revolutionize AI datacenter thermal control solutions and deployments? Phononic is the place for you. We are seeking a results\-oriented individual to join our Infrastructure Solutions organization to deliver industry leading innovative products in support of revolutionizing cooling solutions for AI datacenters.
About the Role
We are seeking a strategic and technically sophisticated Product Marketing Manager (PMM) to drive go\-to\-market success for our semiconductor and AI solutions. This role sits at the intersection of compute, AI workloads, and data center infrastructure, with a growing focus on thermal management and cooling technologies required to scale next\-generation AI systems.
You will translate complex chip architectures, AI workloads, and power/thermal constraints into compelling value propositions—helping customers understand not just performance, but efficiency, sustainability, and total cost of ownership (TCO) in modern AI data centers.
Key Responsibilities
Product Positioning \& Messaging
- Develop differentiated messaging for thermal control systems for AI products (e.g., GPUs, ASICs, AI accelerators, SoCs, CPO, pluggable transceivers)
- Articulate value across performance, power efficiency, and thermal design considerations
- Translate complex concepts such as optical transceiver laser thermal control, AI server and GPU cooling, rack density, cooling requirements, and energy efficiency into customer\-impact narratives
Go\-To\-Market Strategy
- Lead launches of silicon and AI platform solutions optimized for high\-density, thermally constrained environments
- Develop GTM strategies that highlight performance\-per\-watt, cooling efficiency, and infrastructure readiness
- Partner with product and engineering teams on positioning tied to data center scalability and sustainability
Market \& Competitive Intelligence
- Analyze trends across AI infrastructure, high\-performance computing (HPC), and data center thermal management
- Track competitive approaches to liquid cooling, immersion cooling, advanced air cooling, and energy\-efficient architectures
- Develop insights on how competitors address heat density, power consumption, and AI cluster scaling challenges
Sales \& Partner Enablement
- Equip sales teams with materials explaining thermal tradeoffs, rack\-level constraints, and cooling requirements
- Develop tools to communicate TCO, power usage effectiveness (PUE), and data center operational efficiency
- Collaborate with ecosystem partners including data center operators, cloud providers, and cooling and infrastructure vendors (e.g., liquid cooling solutions)
Content \& Thought Leadership
- Create content on AI infrastructure challenges, including power delivery, cooling, and scaling constraints
- Publish whitepapers and technical briefs on AI workload\-driven thermal demands, cooling technologies (liquid cooling, immersion, direct\-to\-chip), sustainable AI infrastructure, etc.
- Represent the company in discussions on AI data center design and efficiency trends
Requirements:
Required
- Bachelor’s degree in Electrical Engineering, Computer Science, Mechanical Engineering, or related field (MBA a plus)
- 5\+ years in product marketing, technical marketing, or product management in:
- Semiconductors
- AI/ML platforms
- Data center infrastructure
- Strong understanding of AI workloads (training vs. inference), compute architectures (GPU/CPU/ASIC), and power and thermal constraints in high\-performance systems
- Ability to translate technical topics such as heat dissipation, cooling architectures, and energy efficiency into business value
- Excellent communication and storytelling skills
Preferred
- Experience with AI data center infrastructure, hyperscale environments, or HPC systems
- Knowledge of thermal management and cooling technologies
- Familiarity with infrastructure design tradeoffs for large\-scale AI clusters
- Background working with cloud providers, colocation vendors, or system integrators
Key Skills
- Technical fluency across compute, power, and thermal systems
- Ability to connect silicon innovation with infrastructure constraints
- Strategic positioning in rapidly evolving AI and data center markets
- Cross\-functional leadership across engineering, product, and sales
- Storytelling grounded in performance, efficiency, and sustainability
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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Phononic, 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Phononic AI Hiring
Phononic has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US.
Location Context
AI roles in Austin pay a median of $215,300 across 523 tracked positions. That's 8% above the national 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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