METHODOLOGY, INC.

Senior Full Stack Engineer / AI Tech Lead

Leader-Doer Role | Remote, Worldwide

The Mission :

Methodology is not a meal kit company. We are building the most advanced personalized nutrition platform in the world - 16,000+ chef-crafted meals per week, each weighed to the gram, tailored to individual biomarkers and lifestyles, shipped nationwide in reusable glass jars and bentos. No gluten, no refined sugar, no canola oil. Measurable health outcomes : strength, focus, energy, sleep, skin, sustainable weight management.

$26M revenue. Seed-funded only. Profitable. Growing 25%+ year-over-year. 40 people at HQ in Concord, CA. 15 globally.

Role Overview :

Title : Senior Full Stack Engineer / AI Tech Lead.

Location : Remote, Worldwide. 6+ hours PST overlap required.

Reports To : Julie Nguyen (CEO/CMO) and Stephen Liu (COO).

Works Daily With : Drew Beyersdorf (Head of HQ Ops); Julie Nguyen; Stephen Liu; existing engineering agency.

Priority : Tier 1 - Most critical hire at the company.

The Opportunity :

We have a solid, functioning Ruby on Rails production platform. We have 9,000+ recipes, 79,861 meal components, 106,000 component ingredients, and 12 million behavioral data points. We have a 54-variable backend scoring engine. We have working AI prototypes for menu optimization and personalized nutrition - built by our COO in a single overnight session - that are ready to be integrated into the production codebase.

Your job is to bring the AI layer to life inside what we have already built. You are not starting from scratch. You are the engineer who connects our working prototypes to production, integrates modern AI capabilities into the Rails platform, and replaces manual operational processes with intelligent systems - all while working collaboratively alongside our existing engineering agency.

What You Will Build :

1. Integrate the AI Prototypes :

- Connect the personalized nutrition onramp prototype (React/Supabase/Netlify) to the production Rails database - branded, tested, live to users.

- Productionize the AI Menu Builder inside the Rails app : database-backed, stable, usable by the culinary team without engineering support.

- Develop the 54-variable scoring engine as a real production service inside the Rails architecture.

2. Add AI-Powered Features to the Platform :

- Personalized meal planning onboarding : biomarker input, health goal selection, AI-generated personalized weekly menu.

- Inventory-aware menu optimization : AI factors real-time inventory levels into menu generation to burn $220K in inventory to $50K while maintaining quality.

- AI-powered yield prediction : 79,861 meal components with yield accuracy issues causing $500-$2,000/week in ordering errors - fix this with a system that learns from production data.

- Customer health data ingestion : Apple Health API, Google Health Connect, lab PDF parsing.

3. Replace Manual Processes with Intelligent Systems :

- Eliminate 30+ hours of COO time per week currently spent on manual menu optimization.

- Automate operational workflows : ordering, yield calculations, inventory management.

- Build marketing analytics integration : Triple Whale, cohort attribution, LTV/CAC tracking.

4. Establish and Uphold Engineering Standards :

- Formalize CI/CD pipeline, code review process, and deployment gates - agreed with the agency so both teams follow the same standards.

- Ensure every integration is built with data security as a first-class requirement : encryption at rest and in transit, RBAC, audit logging, compliance with CCPA, PCI-DSS, and HIPAA as we build toward healthcare partnerships.

- Deliver a written infrastructure assessment in the first two weeks : current state, risks, and integration roadmap in plain language.

Technical Requirements :

- Backend : Ruby on Rails - 4+ years, must be confident in an 11+ year production codebase.

- Database : PostgreSQL : query optimization, schema design, migrations, production data hygiene.

- Frontend : React / TypeScript / JavaScript - working knowledge required.

- AI Integration : LLM APIs (Claude, GPT) in production : error handling, fallback logic, cost management.

- Security : Encryption at rest/transit, RBAC, audit logging, CCPA + PCI-DSS + HIPAA-aligned practices.

- Engineering Discipline : Git branching, PR review, CI/CD, staging environments, QA gates, incident response.

- Legacy Codebase : Methodical ramp-up approach; integrates into existing architecture, does not reflexively rewrite.

- Agency Collaboration : Has worked alongside a contractor team or agency on a shared codebase.

- Stack (Current) : Rails backend, React/TypeScript frontend, PostgreSQL, Heroku, Netlify/Supabase (prototypes).

- Stack (Planned) : CI/CD pipeline, staging environment, monitoring/alerting, AWS/Render migration.

Who We Are Looking For :

The profile : Built foundational engineering discipline at a recognized, established technology company (4+ years, team of 5+ engineers) - then demonstrated the ability to move fast and own outcomes in a leaner environment. Big-company foundation. Startup hunger. Both matter.

What We Need :

- Hands on Keyboard : Writing and shipping code every day. Output visible within the first week.

- Engineering Discipline : Real Git workflow, code reviews, staging environments, QA process - from direct experience in a structured team.

- Data Security Mindset : Specific about encryption, RBAC, and audit logging. Knows CCPA and PCI-DSS apply today. Surfaces compliance concerns before being asked.

- Speed + Discipline : Bias to shipping. v1 fast, measure, iterate. Strong discipline and fast execution are not in conflict.

- Business Judgment : Connects engineering decisions to metrics : margin, time saved, error rate, revenue. Communicates clearly with non-technical founders.

- Low Ego, High Ownership : Takes responsibility. Iterates on feedback. Collaborative with agency. Comfortable being challenged by founders.

- Professionalism : Startup norms : lean, focused, output-driven. Respects colleagues, founders, company money, and agency partners. Works until the job is done, not when the clock hits 5 PM.

If building at the intersection of AI, food science, and health data excites you - and you have the engineering discipline and output track record to prove it - we want to talk.

Interview Process :

- Step 1 Application : We read every application. No AI screening. Show us what you built, what broke, and what the business impact was. GitHub, live apps, output.

- Step 2 Qrata Screen : Recorded 45-min call. Engineering background, security practices, AI fluency, work ethic. Transcript reviewed by Methodology. Structured scoring - must meet minimum threshold to advance.

- Step 3 Technical Conversation : 60-min live conversation. Walk through something you built. Discuss your approach to integrating AI into an existing Rails codebase, working with an agency, and extending a production database.

- Step 4 Paid Technical Exercise : 4-6 hours at your rate. Audit a test environment, fix a specific bug, write a plain-language 90-day AI integration roadmap. Paid regardless of outcome.

- Step 5 Founder Conversation : 45 min with Julie and/or Stephen. Not a technical test - values, communication, alignment on what we're building and why.

- Step 6 Offer : Fast. If you're the right person, we move in days.