--- title: 'Member of Technical Staff - Capital Markets at Internet Backyard' canonical: 'https://feeny.ai/job/member-of-technical-staff-capital-markets-internet-backyard-san-francisco-z9tygwf2g4w1' type: 'job' last_seen: '2026-09-11' --- # Member of Technical Staff - Capital Markets at Internet Backyard - **Company:** Internet Backyard - **Location:** San Francisco, CA - **Compensation:** $160k–$200k - **Employment:** full-time - **Work type:** onsite - **Posted:** 2026-06-25 - **Last confirmed live:** 2026-09-11 - **Apply:** https://jobs.gem.com/internet-backyard/am9icG9zdDrBJgIBE8fx58vcp_r_kfkX ## Job description ## About Internet Backyard Internet Backyard is building financial infrastructure for the compute economy. AI is making compute one of the most important resources in the world, but the systems around it are still early. We help data centers, neo clouds and inference providers automate financial operations and create the trusted records needed for pricing, cash collection, market visibility, and future capital products. If you want to build systems that outlive you, help define a market, and shape compute into an asset class, join us. We're early, small, in person, well-funded, and moving fast. ## What You’ll Do You'll work primarily on the pricing engine, tackling the data engineering challenges underneath it. That means improving and extending the engine, building the modeling methodology, and wrestling messy, real-world data into something clean and usable. No product team to delegate to, no design reviews to schedule. You collaborate with Engineering, talk to customers to understand their pain points and spot opportunities, and you move fast and own it fully. ## What We’re Looking For - You've built at an early-stage startup and loved it. You’re a generalist who's happy wearing many hats and figuring things out without much hand-holding. - You know the machine learning fundamentals cold. Linear regression, gradient boosting (XGBoost), and the broader ML toolkit. You can pick a modeling methodology and clearly justify why it's the right call. - You've turned messy, real-world data into clean, usable pipelines. Wrangling dirty data is the core architectural challenge of our pricing engine. - You're strong with Python and the quantitative research stack: numpy, pandas, polars, scikit-learn, backed by a solid mathematical foundation. - You have a background in economics, econometrics, capital markets/quantitative trading, and you know how to reason about what actually drives prices. - You understand why the financial infrastructure behind compute matters, and you want to be part of building it. Nice to have: - Experience in energy markets - Familiarity with our stack: SQLAlchemy, PostgreSQL, and RESTful APIs. Bonus if you have some TypeScript for when you touch the rest of the app. ## How We Work No layers, no handoffs, no hiding behind process. Everyone is close to the work and expected to use good judgment. Agency and autonomy are yours to lose. Be kind. We don't do jerks. ## Compensation & Benefits Base Salary: $160,000-$200,000 USD Equity: 0.5% Healthcare, vision, dental, monthly stipend, private chef, 401(k), and whatever else you need to help build a generational company. ## About Internet Backyard ## Company Overview - **One-liner**: Internet Backyard builds the financial backbone for the global compute economy by automating quoting, billing, payments, and financial workflows for data center and GPU cloud providers. - **Entity Type**: Private (Pre-Seed) - **Headquarters**: San Francisco, California, United States - **Founded**: 2025 - **Founders**: Mai Trinh (CEO) and Gabriel Ravacci (CTO) ## Core Business - **Primary industry/industries**: Financial operations for AI infrastructure; Software as a Service (SaaS) focused on compute providers (data centers, GPU clouds, neoclouds). - **Target customers**: B2B – data center operators, GPU cloud providers, managed AI infrastructure companies. Ultimately also serving tenants (AI companies) and capital providers (banks, insurers). - **Mission or purpose statement**: "Building the financial backbone for the global compute economy" – turning physical compute capacity into a tradeable, financeable asset. ## Products & Services - **Compute Finance Platform**: A SaaS platform that standardizes quoting, billing, metering, payments, and financial workflows for compute providers. It unifies billing with power usage (matching “what was used” with “what was paid”) and enables compute to be reserved, hedged, insured, and financed like any other asset. ## Market Standing - **Valuation/Market Cap**: Not disclosed. - **Key Metric**: Total Funding – $4.5 million (Pre-Seed round, December 2025). - **Notable Investors/Partners**: Lead investor: Basis Set Ventures. Total of 8 investors in the pre-seed round. - **Growth Signals**: Early-stage startup with 7–9 employees as of mid-2026. Opened a fellowship program (Basis Set x Internet Backyard AI Fellowship) and has 3 active job postings (Technical Product Manager, Member of Technical Staff – Full Stack, Member of Technical Staff – Data Engineer). Headcount was growing but showed a slight dip (-11.1% monthly) likely due to early team churn. Raised $4.5M in pre-seed, signaling strong investor confidence in the problem space. ## Competitive Advantages - **Category creation**: Tackling the $50B billing challenge for data centers with a unified financial infrastructure, moving the industry away from spreadsheets and manual reconciliation. - **Standardized abstractions**: Turning heterogeneous racks, regions, SKUs, and workloads into consistent, tradeable units – enabling a new asset class for compute. - **Integrated data flywheel**: Metering, billing, contracts, risk, and financing live on the same rails, giving providers and tenants a single source of truth and enabling sophisticated commercial terms and risk management. ## Strategic Focus - **Current priorities**: Deepening the platform’s integrations with data centers and GPU clouds, scaling the team (especially technical roles), and proving the unit economics for the compute finance layer. - **Direction for growth**: Expand from quoting/billing into financing, hedging, and insurance for compute capacity. Establish Internet Backyard as the standard financial infrastructure for the AI compute market. ## Why Work Here - **Culture highlights**: Described as "intense" and "seriously hard" – the team values ownership, speed, and building systems that will outlive them. The CEO has stated they are "building the next asset class" and that conventional work-life balance may not fit, but the upside is the chance to shape the financial infrastructure of AI from the earliest days. - **Remote/hybrid/office policy**: Based on Built In, the company operates from physical offices (San Francisco and Toronto/Locations in Canada). The website and LinkedIn suggest a mix of in-office and remote, with a clear emphasis on in-person collaboration. - **Notable perks or engineering culture**: Early-stage equity; direct exposure to founders and Basis Set Ventures; the opportunity to work across finance, infrastructure, and AI. The team is small (less than 10 people) so engineers will have massive ownership and cross-functional impact. ## Sources 1. [Internet Backyard Website](https://internetbackyard.com/) 2. [LinkedIn Company Page](https://linkedin.com/company/internet-backyard) 3. [LinkedIn Culture Post](https://www.linkedin.com/posts/internet-backyard_internet-backyard-careers-activity-7477467805467033600-Aj8h) 4. [Built In Company Profile](https://builtin.com/company/internet-backyard) 5. [Gem Careers Page](https://jobs.gem.com/internet-backyard) 6. [Wellfound (Apollo) Profile](https://wellfound.com/company/internet-backyard) (implied from search results) ## Other roles at Internet Backyard - [Member of Technical Staff - Designer](https://feeny.ai/job/member-of-technical-staff-designer-internet-backyard-san-francisco-fk5m3y23k5gh) — San Francisco, CA - [Member of Technical Staff - Full Stack Engineer](https://feeny.ai/job/member-of-technical-staff-full-stack-engineer-internet-backyard-san-francisco-aw4rhcnb1m75) — San Francisco, CA