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About This Role
Technical Lead / Software Architect \-\- Generative AI Hyperpersonalization Platform
$96,000\-$120,000/year \| Remote (US\-based preferred) \| Full\-Time Contractor \| 0\.5\-1\.0% Equity
HeartStamp is an AI\-native hyperpersonalization platform for greeting cards, invitations, and gifting. Users create deeply personal, professionally printed physical products through a guided AI experience. We generate production\-ready, print\-perfect output (PDF/X, 300 DPI CMYK) fulfilled through our print\-on\-demand partner network. MVP launches April 2026\. Scaling across the US, UK, and Canada.
We're a bootstrapped, founder\-led team of 25\+ across engineering, product, design, and growth. We move fast, we ship, and we build to win.
THE ROLE
We need a Tech Lead who owns the technical vision and stays deeply hands\-on in the codebase. This is a player\-coach role \-\- expect 60\-70% of your time writing, reviewing, and debugging production code. You set architecture direction and lead the engineering team, but you build alongside them every day.
This role is heavily focused on AI/RAG systems, semantic search infrastructure, image generation pipelines, and DevOps/infrastructure strategy. You'll lead a team of full\-stack engineers, a RAG engineer, an AI orchestration engineer, a DevOps engineer, and a QA lead.
WHAT YOU'LL DO
Own the end\-to\-end technical architecture: Python backend, Next.js frontend, AI/RAG pipelines, vector search infrastructure, and AWS cloud platform.
Lead development of our RAG pipeline using PostgreSQL/pgvector, LangChain, LangGraph, and LangSmith. Improve hallucination handling, prompt engineering, content validation, and multi\-model inference routing across a model\-agnostic architecture.
Take direct ownership of AWS infrastructure strategy, cost optimization, autoscaling, and production reliability. Architect for zero\-floor scaling with no idle burn. Manage Kubernetes clusters efficiently and maintain CI/CD pipelines.
Write production code daily. Review code. Debug production issues. Harness the full potential of Claude Opus 4\.6 and Codex 5\.3 to move at a velocity that would require three engineers without AI acceleration.
Run sprint planning, code reviews, architecture discussions, and daily standups for a distributed engineering team across multiple time zones.
Ensure our generation pipeline produces print\-perfect results: correct color profiles, bleed zones, resolution, and format compliance through our DocRaptor/PrinceXML print pipeline.
Build for international expansion (multi\-currency, multi\-language, multi\-partner routing), product extensions (digital 3D cards, invitations, gifting), and a future creator marketplace with royalty structures via Stripe Connect.
WHAT WE'RE LOOKING FOR
- 6\+ years of engineering experience designing and building complex, scalable production systems
- Deep backend expertise in Python/FastAPI (our backend is Python \-\- you need to be authoritative here)
- Real RAG and vector search experience: pgvector or comparable vector databases, embedding strategies, retrieval optimization, LangChain, LangGraph, LangSmith
- Full\-stack capability in React/Next.js and TypeScript
- Strong DevOps and infrastructure experience on AWS with Kubernetes, GitHub Actions CI/CD, containerized microservices, Terraform/IaC, and a track record of infrastructure cost optimization
- AI\-native workflow using Claude, Codex, Cursor, or similar tools as daily force multipliers
- Current, hands\-on coding proficiency (we will evaluate this in our interview process)
- QA\-driven engineering mindset
- Strong written communication and experience leading distributed async teams
OUR TECH STACK
- Frontend: Next.js 15, React 18, TypeScript, Tailwind CSS, ShadCN UI, Konva.js
- Backend/API: FastAPI (Python 3\.10\+), PostgreSQL, pgvector, Redis, Celery
- AI/ML: LangChain, LangGraph, LangSmith, model\-agnostic orchestration, RAG pipelines
- Infrastructure: AWS (EKS, EC2, RDS, S3\), Kubernetes (K3s), GitHub Actions, Docker, Terraform
- PDF/Print: DocRaptor (PrinceXML), Cloudinary, ICC color management
- Payments and Auth: Stripe Connect, Clerk
- Search and CMS: Typesense, Sanity
- Observability: Sentry, LangSmith, PostHog
ENGAGEMENT TERMS
- $96,000\-$120,000 USD/year ($8,000\-$10,000/month)
- Full\-time independent contractor, monthly retainer
- 50 hours/week baseline with flexibility during critical phases
- 0\.5\-1\.0% equity over a 4\-year vesting schedule
- Formal performance and compensation review at 91 days
- Remote, global. US time zone overlap required for team syncs
Compensation reflects US\-based cost of living. We welcome applicants from outside the United States, but compensation will be adjusted to reflect local market rates.
WHY HEARTSTAMP
You're not joining a feature factory. You're architecting a platform that bridges AI and physical products. Our RAG system has to understand art styles, emotional tone, cultural context, relationship dynamics, and print production constraints in one generation pass. This isn't a chatbot wrapper.
Small, high\-output team. Your decisions shape the product directly. No layers of middle management. You report to the CEO.
TO APPLY
Submit your resume. Qualified candidates will receive a technical screening questionnaire as a next step. Contact: careers@heartstamp.com
Pay: $96,000\.00 \- $120,000\.00 per year
Benefits:
- Flexible schedule
Education:
- Bachelor's (Required)
Experience:
- Full\-stack development: 5 years (Required)
Language:
- English (Required)
Work Location: Remote
Salary Context
This $96K-$120K range is above the median for AI/ML Engineer roles in our dataset (median: $100K across 15465 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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At HeartStamp Inc, 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 $166,983 based on 13,781 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($108K) sits 35% below the category median. Disclosed range: $96K to $120K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
HeartStamp Inc AI Hiring
HeartStamp Inc has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $120K - $120K.
Remote Work Context
Remote AI roles pay a median of $156,000 across 1,221 positions. About 7% 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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