Interested in this AI/ML Engineer role at PHISON Technology Inc.?
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About This Role
Job Description
Phison Electronics is a global leader in NAND flash controllers and storage solutions, powering more than one in every five SSDs shipped worldwide. Phison has grown into a multi\-billion\-dollar company with over 4,500 employees – 70% of which are dedicated to R\&D – and more than 2,000 patents. The company’s innovations include aiDAPTIV\+, an award\-winning AI solution for affordable LLM training and inferencing on\-premises, and Pascari, a portfolio of ultra\-high\-performance enterprise SSDs purpose\-built for data\-intensive workloads across AI, cloud, and hyperscale data centers.
Every controller and drive we ship is designed entirely in\-house. Our products power data centers, enterprise infrastructure, consumer devices, and embedded systems around the world and in space. We are now building an intelligent storage appliance that deploys custom AI models directly at the storage layer. This role leads that effort, with the rare added benefit of direct input into Phison SSD hardware design to optimize our products for AI inference workloads. As a Lead, you will grow the team as the platform evolves and our customer base expands.
Reports To
CTO/CIO – Chief Technology \& Information Officer
Job Level
Senior to Principal
Experience
7\+ years development, with experience leading teams
Leadership
Team\-building role – growing a new platform software team
Commercial
Understanding of what is needed to create robust and secure market\-ready solutions.
Location
Hybrid, San Jose CA
We don’t expect any candidate to check every box. If this role excites you and you meet many (but not all) of the qualifications, we encourage you to apply. Also, we really do have SSD in orbit, on the Lunar surface and on Mars. We especially encourage applications from candidates who have done the work but may not yet have had the title, headcount, or formal authority to show for it.
WHAT YOU’LL DO
- Lead the architecture and delivery of a Linux\-based AI storage appliance — from kernel interfaces to management APIs.
- Collaborate with partners on LLM deployment at scale: inference serving, optimization pipelines, and production operations with real SLA requirements.
- Implement LLM execution optimizations based on Phison aiDAPTIV technology including KV cache offload to NVMe, quantization, batching, and fine\-tuning workflows.
- Work with Phison’s in\-house hardware and firmware teams to shape Computational SSD and controller design for AI inference workloads.
- Lead and grow a cross\-functional team of Linux and full stack engineers — hiring, mentoring, and delivery ownership.
WHAT YOU’LL BRING
- 7\+ years of Linux\-focused software development, and experience leading or growing a technical team, whether through a formal management title or by driving cross\-functional delivery, mentoring, or project leadership.
- Proven commercial track record — at least two products shipped from development through to market.
- Hands\-on expertise in LLM deployment at scale and inference optimization (vLLM, TensorRT\-LLM, llama.cpp, or equivalent).
- Practical experience with KV cache offload to storage, quantization (GPTQ/AWQ/GGUF), and LLM fine\-tuning (LoRA/QLoRA).
- Strong Linux full stack skills: systems\-level C/C\+\+/Go/Rust, backend services, container orchestration, and CI/CD.
DESIRABLE
- Any of the following will strengthen your application:
- Linux kernel development, device drivers, or NVMe storage internals.
- Linux security hardening (SELinux, AppArmor, secure boot) or networking stack internals.
- Distributed storage platforms (Ceph, GlusterFS, or equivalent).
WHY PHISON
- As a leader at Phison, you will be supported by executive partnership and cross\-functional collaboration. You won’t be operating alone.
- We invest in our leaders through mentorship, peer collaboration, and clear decision\-making frameworks.
- We also value engineers who are comfortable working at the edge of what’s known, asking questions, testing hypotheses, and learning quickly in emerging technical spaces.
COMPENSATION
Compensation ranges reflect base salary, final compensation will be determined in good faith based on a variety of factors, including the candidate’s relevant experience, specialized skills, education, and geographic location. Candidates in certain markets may fall outside the range listed. This range reflects base pay only and does not include potential bonuses or our comprehensive benefits.
BENEFITS
We offer a competitive benefits package including medical, dental, and vision insurance, 401(k) with company match, paid time off, and more. View our full benefits at https://www.phisonenterprise.com/careers/
Equal Opportunity Employer – We encourage candidates to still apply, even if you don’t meet 100% of the qualifications. Phison Electronics is committed to a diverse, inclusive, and equitable workplace. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability, veteran status, or any other characteristic protected by applicable law.
Pay: $140,000\.00 \- $200,000\.00 per year
Benefits:
- 401(k)
- 401(k) matching
- Dental insurance
- Flexible spending account
- Health insurance
- Life insurance
- Paid time off
- Parental leave
- Vision insurance
Application Question(s):
- Will you now or in the future require employment visa sponsorship (e.g., H\-1B, TN, etc.)?
Work Location: Hybrid remote in San Jose, CA 95134
Salary Context
This $140K-$200K range is below 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 PHISON Technology Inc., 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. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($170K) sits 21% below the category median. Disclosed range: $140K to $200K.
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.
PHISON Technology Inc. AI Hiring
PHISON Technology Inc. has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in San Jose, CA, US. Compensation range: $200K - $200K.
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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