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About This Role
Job Description:
Onni Mission Critical is building a new kind of digital infrastructure platform — urban, connected, edge\-located, and purpose\-repositioned for AI inference and enterprise compute. Our flagship asset at 600 Wilshire in downtown Los Angeles is live with 2 MW of initial capacity, immediate expansion headroom to 8 MW, and a development path scaling further as the building is provisioned. Additional sites are under active assessment across Seattle, Chicago, and a broader North American pipeline.
We are hiring our first AI Campus Lead — who will drive the marketing strategy and leasing activity for our integrated AI campus sites, promote opportunities to prospective customers, and help establish the commercial foundation that differentiates our platform from traditional colocation and hyperscale approaches. This role is the ground\-floor commercial seat at a platform backed by a fully integrated real estate owner, developer, and operator.
About the platform
Our business model is distinct from both traditional hotel carrier/colocation facilities and hyperscale data center platforms. We are not simply providing standardized space and power for third\-party tenants, nor are we pursuing the scale\-driven model of large hyperscalers. Instead, we are building an AI campus strategy focused on creating an integrated environment designed specifically for AI infrastructure, users, and ecosystem partners.
Onni Mission Critical is the digital infrastructure division of Onni Group, one of North America's most active private real estate developers. Every site in the platform is 100% fee\-simple owned — no landlord consent required — and integrated end\-to\-end across development, construction, and operations. The platform operates in partnership with AI\-native infrastructure operators, under a structure that aligns real estate, capital, and operating expertise at each deployment.
600 Wilshire exemplifies this approach: a premium 292,000 square\-foot office asset in downtown LA, repositioned as a connected AI campus with native inference and enterprise compute capability. It serves as the template for the next 130\+ megawatts of portfolio deployment across LA, Seattle, Chicago, and additional markets — each site designed as an integrated AI ecosystem rather than commoditized data center space.
The role
Because this campus model depends on identifying prospects, structuring lease opportunities, and building the right market relationships to attract and secure tenants, we need an AI Campus Lead to drive leasing activity, promote the opportunity to prospective customers, and help establish the commercial foundation for the campus.
The AI Campus Lead owns the commercial development of the platform — from first prospect contact through executed lease. You are the person who can speak the language of AI infrastructure buyers on inference economics, deployment patterns, latency, and workload requirements, and who translates the campus value proposition into commercial relationships. This is an ecosystem\-building role as much as it is a leasing role.
Whatyou'llown
- Campus development \& leasing.Build and execute the commercial strategy for 600 Wilshire and subsequent AI campus sites, positioning each location's integrated AI infrastructure capability to target customers including hyperscalers, AI inference operators, enterprise infrastructure teams, and ecosystem partners.
- Market intelligence.Track the competitive landscape — Digital Realty, Equinix, CoreSite, CoreWeave, Crusoe, and the hyperscaler build\-to\-suit market. Translate what you learn into positioning, pricing, and product decisions.
- Partnership \& ecosystem.Develop relationships with network carriers, interconnection providers, systems integrators, and AI platform partners whose presence at our sites strengthens the campus value proposition.
- Brand voice.Be the platform's voice at industry events, on panels, and in long\-form writing. Shape the narrative that positions Onni Mission Critical as the credible choice for urban edge AI infrastructure.
Whatwe'relooking for
- 3 to 6 years of commercial experience in AI infrastructure, hyperscaler GTM, edge computing, data center leasing, or enterprise infrastructure sales.
- Fluent in the vocabulary of AI workloads — inference versus training, model serving economics, token\-level pricing, GPU utilization, latency budgets, and enterprise deployment patterns.
- Personal user of AI tools in daily work. You don't sell what you don't use.
- Demonstrated ability to build pipeline from zero — sourcing, qualifying, and moving accounts through stages without inheriting a book of business.
- Existing network among AI infrastructure buyers, hyperscaler real estate teams, or enterprise CIOs strongly preferred. Relationships at CoreWeave, Lambda, Together AI, Crusoe, AWS AI/ML, GCP AI, Azure AI, Cloudflare, Databricks, or frontier AI labs are particularly relevant.
- Comfortable operating in a build\-stage platform with direct ownership access — no bureaucracy, no cover, and no substitute for getting the meetings yourself.
- Based in San Francisco or willing to relocate. Regular travel to Los Angeles, Seattle, Chicago, and industry events expected.
Salary Range:
$125,000 \- $175,000
About The Company:
Onni
For over half a century, Onni has been building communities for people to live, work, and play. Our success reflects our commitment to our employees and partners, and our dedication to quality construction, innovation, sustainability, and customer satisfaction. Our expertise expands across North America, with offices in Vancouver, Toronto, Los Angeles, Seattle, Phoenix, and Chicago.
AI Use:
This role may involve the use of artificial intelligence (AI) tools to support research, analysis, content development, design, reporting, or operational efficiency. Employees are expected to use AI responsibly and in compliance with company policies, data privacy requirements, confidentiality obligations, and applicable laws.
All AI\-generated or AI\-assisted outputs must be reviewed for accuracy, quality, and appropriateness before use. Employees are also expected to disclose when AI tools have been used in the creation of work and must not present AI\-generated content as solely their own original work.
How To Apply:
*Please apply through the link on the job posting and attach your resume and any other required documents.*
*We thank all applicants for your interest in the Onni Group. Note that only those applicants under consideration will be contacted.*
Salary Context
This $125K-$175K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Onni Group, 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 $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($150K) sits 17% below the category median. Disclosed range: $125K to $175K.
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.
Onni Group AI Hiring
Onni Group has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Los Angeles, CA, US. Compensation range: $175K - $175K.
Location Context
AI roles in Los Angeles pay a median of $191,580 across 1,792 tracked positions. That's 4% below 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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