Interested in this AI/ML Engineer role at German International School of Silicon Valley?
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About This Role
Marketing Analytics \& AI Automation Associate (Part\-Time)
GISSV is a K\-12 German international school in Silicon Valley. Families choose us the way they choose a house: months of research, multiple campus visits, a five\-figure annual decision, and a commitment that often lasts a decade. That makes our marketing funnel unusually long, unusually measurable, and unusually high\-stakes. We're looking for someone analytical to build the measurement layer under it and to automate the repetitive parts of the work with AI agents. You'll work directly with our fractional CMO, who sets strategy; you own the technical execution.
There's a real number at the bottom of the funnel. Inquiry tour application enrolled. Every conversion has a dollar value attached and a name behind it. You will be able to point at what you moved. You're the technical half of a two\-person team, not an extra pair of hands. We already have a strong marketing generalist covering brand, content, and stakeholder relationships. You're being hired for the complement: data, tooling, measurement, automation.
You'll be coached, not just supervised. Your manager is a fractional CMO. Standing weekly working sessions, direct feedback on your work, and a portfolio of real artifacts at the end — not a certificate of completion. We actually want you to use AI profoundly. If you've been building agentic workflows on your own time and looking for somewhere to point them at a real problem, this job is for you.
### Essential Duties and Responsibilities:
- Measurement foundation:
+ Audit and repair GA4, conversion tracking, and UTM discipline across all channels
+ Define the funnel stages and the events that mark them; make the definitions stick
+ Build a single recurring dashboard the leadership team and board will actually read
- Consistent reporting cadence:
+ Weekly channel snapshot, monthly funnel review, term\-based board reporting
+ Own the numbers end to end: pull, sanity\-check, interpret, present
+ Flag what's off before anyone has to ask
- Agentic workflow builds progressively, as the data foundation allows:
+ Competitive analysis — automated monitoring of peer and competitor schools: positioning, pricing, events, ad creative, organic presence
+ Audience segmentation — moving from one undifferentiated parent audience to defined segments with distinct motivations
+ Personalization — segment\-appropriate messaging across email, landing pages, and paid
+ Signal\-based journey measurement — identifying the behaviors that actually predict an application, and instrumenting for them
- Other duties and/or projects as assigned.
### Experience and Qualifications:
- Currently enrolled (undergrad or grad) or recently graduated — Santa Clara, Stanford, Berkeley, or nearby
- Genuine comfort with data: you can work in spreadsheets fluently, and SQL or Python is a strong plus
- Hands\-on experience building something with LLMs beyond chatting with one — automations, agents, scripts, scrapers, evals
- Self\-directing. Much of this is remote and asynchronous. We'll give you context and clear priorities; we can't give you constant supervision.
- Available through at least June 2027
We are explicitly not requiring a marketing background. Some of the strongest candidates for this role will be studying data science, statistics, CS, symbolic systems, economics, or IEOR. If you can reason about numbers and build things, we'll teach you the marketing.
- Nice to have:
+ German language ability
+ Familiarity with GA4, Google Ads, Meta Ads Manager, or a marketing automation platform
+ Any exposure to education, nonprofit, or another long\-consideration\-cycle purchase
FLSA Classification: Non\-Exempt (hourly)
Hours: 20 hours/week, flexible around your class schedule
Reports To: Fractional CMO; works closely with the marketing generalist and the admissions team
Location: Remote\-first; roughly two days per month on campus in Mountain View, plus a small number of admissions events
Pay Scale: $36\.00 \- $45\.00 per hour worked, depending on experience
*Please note that we are only able to accept applications of candidates who fulfill the language requirements and who are eligible to work in the US (sponsorship of work visa is not available for this position).*
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Salary Context
This $74K-$93K range is in the lower quartile 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 German International School of Silicon Valley, 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. Entry-level AI roles across all categories have a median of $110,000. This role's midpoint ($84K) sits 61% below the category median. Disclosed range: $74K to $93K.
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.
German International School of Silicon Valley AI Hiring
German International School of Silicon Valley has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Mountain View, CA, US. Compensation range: $93K - $93K.
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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