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About This Role
Principal AI Safety Architect
Date: Oct 5, 2026
Location: San Jose, California, United States
Company: Super Micro Computer
Job Req ID: 30442
About Supermicro:
Supermicro® is a Top Tier provider of advanced server, storage, and networking solutions for Data Center, Cloud Computing, Enterprise IT, Hadoop/ Big Data, Hyperscale, HPC and IoT/Embedded customers worldwide. We are the #5 fastest growing company among the Silicon Valley Top 50 technology firms. Our unprecedented global expansion has provided us with the opportunity to offer a large number of new positions to the technology community. We seek talented, passionate, and committed engineers, technologists, and business leaders to join us.
Job Summary:
Supermicro is hiring a Principal AI Safety Engineer in Technology Enablement to help customers run agentic AI on Supermicro infrastructure in named enterprises and in GPU clouds (neoclouds).
This is a customer-facing design role, not a security operations role. You will join architecture workshops, define how agents and tenants are isolated, identified, governed, and audited, and write that control layer into the design before systems are quoted or shipped. Enterprise work is one customer, one data boundary. Neocloud work is many tenants on a shared factory. You will cover both. You will work with Supermicro solution architects, factory engineering, cloud and enterprise sales, and NVIDIA field teams.
Supermicro partners with NVIDIA, AMD, and Intel, and with leading storage and networking companies. This role is majority NVIDIA-focused, including NVIDIA runtime and guardrail software, confidential computing, and data-center security features where they belong in the design. The same control model must still work when a factory includes AMD, Intel, or partner storage and networking.
San Jose, CA location is preferred. Exceptional Principal-level candidates elsewhere in the United States will be considered if they can travel for customer workshops.
Essential Duties and Responsibilities:
1. Customer-facing AI safety architecture — enterprise and neocloud
Join engagements when customers will deploy agents, tools, or regulated inference.
- Enterprise: safety profile for a single owner — data boundary, air-gap or private cloud, human gates, audit, and what OpenShell vs Sentry vs confidential computing actually enforce in that boundary.
- Neocloud: tenant isolation, inference-gateway policy, quota and blast-radius controls, confidential computing and DPU isolation between tenants, and attested evidence a cloud customer can hand to their own CISO (identity, policy decisions, telemetry — not agent self-reporting).
- Set proof-of-concept pass/fail criteria so safety is tested, not assumed. Define what is enforceable on current installed NVIDIA systems versus what requires BlueField-4 Sentry or Vera-class platforms.
2. Controls on Supermicro AI factories
Map the NVIDIA Open Agent Safety Platform onto Supermicro server, rack, and cluster designs used by enterprises and GPU cloud operators.
- Place OpenShell (runtime, least-privilege policy, sandbox) and Sentry (BlueField-4 / DOCA, out-of-band monitoring, millisecond containment) in the design.
- Identify where confidential computing, SmartNIC/DPU security, multi-tenant networking, air-gapped operation, or partner security platforms belong.
- Design one control model that still works when a factory includes AMD, Intel, or partner storage and networking. OpenShell’s extendability off NVIDIA CPUs is part of that model, not an afterthought.
- Produce the safety chapter of the quote package: what is in hardware, what is in runtime, what remains a customer operational control.
3. Field enablement and demonstration
Create safety design templates for both motions: named-enterprise factory and multi-tenant neocloud. Help solution architects start the conversation; you own the safety architecture when agents or tenancy are in scope.
Contribute customer-facing material and represent Supermicro in technical sessions with enterprises, GPU cloud operators, NVIDIA, and other partners. You will put a working control demo in front of a customer and an NVIDIA architect. OpenShell at minimum; Sentry / BlueField-4 / confidential computing where the platform supports it.
Qualifications:
- Bachelor’s or master’s in Computer Science, Computer Engineering, Information Technology, or equivalent.
- 12+ years in security engineering, platform security, cloud security, or applied AI systems. 5+ years accepted with hands-on production work on agent or LLM runtime controls (sandbox, policy engine, gateway, or equivalent — not training-only or policy-paper backgrounds).
- Able to prototype and demonstrate controls: Python or equivalent, containers, and policy or guardrail configuration.
- Hands-on with at least two of: NVIDIA OpenShell, NVIDIA Sentry / BlueField / DOCA, confidential computing, inference gateway, NIM, or NeMo Guardrails. OpenShell or Sentry strongly preferred after this announcement.
- Hands-on experience with sandboxes, policy engines, secret proxies, identity for agents, or equivalent.
- Experience with at least one of: regulated enterprise AI or multi-tenant GPU / AI cloud isolation. Both strongly preferred.
- Working knowledge of NVIDIA enterprise and safety software and how it lands on server, DPU, and rack designs. NVIDIA certification desirable.
- Ability to design controls for mixed GPU/CPU environments and for shared-cluster tenancy.
- Comfortable presenting to customer security leaders, cloud architects, and NVIDIA technical teams.
Salary Range
$210,000 - $255,000
The salary offered will depend on several factors, including your location, level, education, training, specific skills, years of experience, and comparison to other employees already in this role. In addition to a comprehensive benefits package, candidates may be eligible for other forms of compensation, such as participation in bonus and equity award programs.
EEO Statement
Supermicro is an Equal Opportunity Employer and embraces diversity in our employee population. It is the policy of Supermicro to provide equal opportunity to all qualified applicants and employees without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, protected veteran status or special disabled veteran, marital status, pregnancy, genetic information, or any other legally protected status.
Salary Context
This $210K-$255K range is above the 75th percentile for AI Safety roles in our dataset (median: $223K across 10 roles with salary data).
Role Details
About This Role
This role sits at the intersection of AI and engineering, building systems that bring machine learning capabilities into production environments. The scope varies by company, but the common thread is applying AI technology to solve real business problems at scale. Most AI roles today require a combination of software engineering fundamentals and domain-specific ML knowledge, with the exact mix depending on the team's maturity and the product they're building.
The AI job market is evolving fast. New role categories emerge as companies figure out what they need to ship AI-powered products. What matters most is the ability to learn quickly, build working systems, and iterate based on real-world performance data. The specific title matters less than the skills you bring and the problems you can solve. Companies are past the experimentation phase and want engineers who can deliver production-quality systems that work reliably at scale.
Across the 9,079 AI roles we're tracking, AI Safety positions make up 1% of the market. At Super Micro Computer, Inc., this role fits into their broader AI and engineering organization.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
What the Work Looks Like
Day-to-day work involves a mix of building, debugging, and collaborating. You'll write code, review pull requests, participate in design discussions, and work with cross-functional teams (product, design, data) to define what AI features should do and how they should behave. Expect to spend time on both technical implementation and communication. Most AI teams operate in two-week sprint cycles, with regular demos and retrospectives. The ratio of heads-down coding to meetings and reviews varies by seniority, with senior roles spending more time on architecture decisions and mentorship.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
Skills Required
Python and cloud platform experience are common requirements. Specific skill needs vary by company and focus area, but familiarity with ML frameworks, data pipelines, and API design covers the basics for most roles. RAG (Retrieval-Augmented Generation), vector databases, and LLM API integration are increasingly standard requirements across role types.
Beyond the core stack, communication skills matter more than many technical candidates realize. The ability to explain AI capabilities and limitations to non-technical stakeholders is a differentiator at every level. Technical writing, documentation, and clear thinking about tradeoffs are underrated skills in AI roles. Experience with evaluation methodology (how to measure whether an AI system is working well) is becoming a core requirement, especially for roles that involve LLM integration.
Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
Compensation Benchmarks
AI Safety roles pay a median of $256,000 based on 66 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($232K) sits 9% below the category median. Disclosed range: $210K to $255K.
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 Research Engineer ($252,000) and AI Engineering Manager ($251,622). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Super Micro Computer, Inc. AI Hiring
Super Micro Computer, Inc. has 4 open AI roles right now. They're hiring across AI/ML Engineer, AI Safety, AI Architect. Based in San Jose, CA, US. Compensation range: $169K - $255K.
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 Safety roles include Software Engineer, Data Scientist, Data Analyst.
From here, career progression typically leads toward Senior Engineer, AI Architect, Engineering Manager, Principal Engineer.
Focus on building things that work. A deployed project that solves a real problem is worth more than any certification. Contribute to open-source, build portfolio projects, and invest in fundamentals (software engineering, statistics, systems design) rather than chasing the latest framework. The AI field moves fast, but the engineers who succeed long-term are the ones with strong fundamentals who can adapt to new tools and paradigms as they emerge.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
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).
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
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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