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About This Role
We're building the largest portfolio of affordable housing in the heart of America, join us!
SFR3 is a boutique real estate investment fund acquiring $2B\+ in affordable single\-family homes by 2024\. The Fund renovates distressed homes, using software\-driven operations to run many tertiary markets concurrently.
We’ve turned thousands of distressed homes from Cincinnati to Columbia into newly renovated, affordable rentals. We’re just getting started – want to join the teams behind all the hard work?
Our first fund owns 9,500\+ homes in 19 states.
You
Build First
Product\-Minded
Operationally Sharp
Technically Fearless
You are a “hands on” operator, having plied your trade by actually *building* workflows \& products that drive operations – not reading Tweets Xits about it. You are at home in a technical environment, straddling the line between BizOps \& Product. Your decisions and ideas are of course backed by data and analysis, but you have an exceptionally strong bias in favor of rapid action and iteration – you learn by doing. You sling no\-code into the ether at a rate that has the CTO *slightly* concerned. Automated workflows *that just run* excite you unreasonably. You actually ship things. Code, workflows, automations, products. You've done it in production, under pressure, with real users who needed it yesterday. Deep down, you know that crontab is still the fastest way to schedule anything.
What drives you is the craft — building something that works, that people use, that actually moves the needle. You're the person who, when handed a messy real\-world problem, immediately starts sketching the system that fixes it.
Your Mandate
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Technical leverage is the name of the game: you will have a front row seat to our entire business, from acquisition \& renovation to property management \& payments. You understand how \& where rails \& agents can make a difference; when to use our stack vs buy \& integrate. You will take diverse analog businesses and make them more reliable, resilient and efficient using modern tools of the trade. What kinds of problems are we working on across the stack?
- Optimizing the utilization of our maintenance workforce (TSP with Windows)
- Automating reconciliation of maintenance expenses from the Field
- Detecting anomalies in vendor payments
- Determining which signals predict residents most worth renewing
Build \& Ship
- Design and build systems, internal tools, and operational software that directly run the business
- You are borderline AI psychosis, leveraging AI tooling, LLMs, and automation frameworks to accelerate development and build smarter systems
Understand the Business
- Embed in your business unit — go deep on how work actually gets done before building anything
- Translate messy operational reality into clean, scalable architecture
- Own the outcome, not just the output — track whether what you built actually worked
Drive Product Thinking
- Scope and prioritize the roadmap based on business impact, not complexity
- Make pragmatic tradeoffs between shipping fast and building durably — and know which one this moment calls for
- Bring data and user feedback into every decision — measure what matters
You will join a team of ex\-Uber operators. You will learn from the best – and make them better.
What We Are Looking For
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- Bachelor’s degree Extra credit for hard sciences. Also Philosophy.
- 5\+ years experience in Biz Ops or Product Management – prepare to explain what you built \& launched, and how it drove the business!
- Experience shipping (useful) products or ops under terminal duress.
- Facility with data, dashboarding, SQL
- Kick\*\*\* origin story – *minor* embellishments will be tolerated
Strong Signals
- Experience in a startup, operator, or BizOps environment — somewhere priorities changed fast and you shipped anyway
- Exposure to no\-code/low\-code tooling (Zapier, Pipefy, n8n, etc.) — you know when to build vs. configure
- AI\-first instincts — you reach for LLMs, code\-gen, and automation before reaching for headcount
- Entrepreneurial experience — you've had skin in the game before
You Don't Need To
- Have real estate experience — domain knowledge is learnable; builder instincts are not
Compensation
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- Industry leading compensation \& equity
- Fully Remote, Flexible PTO, Covered Health/Dental, 401k package, Macbook Pro, \+ more.
We are an ex\-Uber team building the future of affordable housing at scale. Finding it hard to contain your excitement? Reach out!
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 SFR3 Fund, 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. Director-level AI roles across all categories have a median of $274,554.
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.
SFR3 Fund AI Hiring
SFR3 Fund has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% of all AI roles offer remote work.
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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