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About This Role
San Francisco, CA · On\-site · Full\-time
Compensation: $160,000–$200,000 \+ 0\.5–2% equity
About the Company
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Our client is a seed\-stage startup building agentic AI for the built world: multimodal agents that review construction blueprints for buildability and code compliance, becoming domain experts on rules like accessibility and fire\-safety codes. They are the technical leader in their space, and their mission is to make housing more affordable — one of the very few places in tech where an engineer works directly on the housing crisis. This is a founding\-engineer seat on a tiny, high\-ownership team.
Founded 2025 · 1–10 people · Industry: Property Tech
The Role
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A deeply self\-directed founding AI engineering role owning the full experiment loop to improve the accuracy of the client's multimodal blueprint\-compliance agents. The work is heads\-down and technical, with a small amount of weekly user contact — not a forward\-deployed role.
What you'll be doing
- Spend the majority of your time improving the accuracy of the multimodal blueprint\-compliance agents — building autoresearch, improving evals, and running sweeps on experimental features you design.
- Own the full experiment loop: generating ideas, implementing them, and rigorously analyzing results on the eval framework.
- Become a domain expert on the parts of the building code that matter (accessibility, wildland\-urban interface, structural and fire safety).
- Wear hats as needed: computer\-vision experiments, lightweight data engineering, internal tooling, and webapp improvements.
- Roughly two hours a week of user contact and product dogfooding.
Tech stack: Tech\-stack agnostic; TypeScript across front\-end and back\-end. Modern AI coding tools used throughout.
Requirements
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- Highly independent and self\-unblocking; able to direct your own work with minimal oversight.
- Fast\-moving and prototype\-oriented; you ship experiments quickly.
- Strong evaluation discipline and genuine research taste for high\-risk, high\-reward work.
- Either a strong software engineer eager to grow into ML/AI, or an ML/AI engineer who already prototypes fast.
- Comfortable across the stack (TypeScript front\-end and back\-end).
- Genuinely motivated by the mission of making housing more affordable.
- Able to work on\-site in San Francisco full\-time, approximately 55 hours/week with some weekend availability.
Nice to Haves
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- Experience at an autonomous\-vehicle company, where rigorous evaluation is second nature.
- Background in highly regulated applied\-AI domains (e.g. medical or legal) where evaluation discipline is critical.
- Climate\-tech or other mission\-driven startup experience.
- Strong academic background.
- Genuine curiosity for digesting dense technical and regulatory material (building codes, technical diagrams).
Why Join
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- Work directly on the housing affordability crisis — a rare opportunity in tech.
- Founding\-engineer ownership with a meaningful equity stake.
- Join the technical leader in its space, with a demo that gets strong engineers excited.
- Strong comp plus full benefits: free lunch and dinner at the office, fully paid health insurance with $1,000\+/yr employer HSA contributions, and commuter and wellness benefits.
Details
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- Location: San Francisco, CA
- Work policy: On\-site, full\-time; \~55 hrs/week with some weekend availability
- Compensation: $160,000–$200,000 \+ 0\.5–2% equity
- Visa sponsorship: None available; relocation supported
- Employment type: Full\-time
Salary Context
This $160K-$200K range is above 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 David Joseph & Company, 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. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($180K) sits 16% below the category median. Disclosed range: $160K 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.
David Joseph & Company AI Hiring
David Joseph & Company has 4 open AI roles right now. They're hiring across AI/ML Engineer, AI Agent Developer. Positions span San Francisco, CA, US, New York, NY, US. Compensation range: $180K - $300K.
Location Context
AI roles in San Francisco pay a median of $265,000 across 1,335 tracked positions. That's 23% above 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 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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