Founding AI/ML Engineer (Full-Stack, Computer Vision & AI Systems)

We are building an ambitious in-house AI platform designed to become a new standard for social media management and creative production.

We are looking for a highly capable Founding AI/ML Engineer who can take ownership of building the platform end-to-end while making strong architectural and technical decisions along the way.

This is not a prompt-engineering role, a simple OpenAI integration, or an architecture-only position. We need a hands-on engineer with strong architectural thinking who can design and build production systems.

Our Vision
We are not building another AI content generation tool. Our vision is to build an AI platform that gradually automates the entire social media workflow—from understanding a client’s business and brand, planning content, generating copy and visuals, reviewing quality, learning from feedback, and continuously improving results.

Initially, the platform will operate with a human-in-the-loop approach, reducing the manual workload of social media managers from nearly 100% to approximately 20–30%. Instead of creating everything from scratch, they will primarily review, refine and approve AI-generated work. Every approval, rejection, edit and client preference should become learning data that helps the platform improve over time.

Long term, we envision a collaborative ecosystem of AI agents capable of managing increasingly larger parts of the workflow autonomously. The platform should be architected from day one to support this evolution without requiring the core system to be rebuilt.

What You’ll Build
We already have an early prototype covering image analysis, visual understanding, structured generation planning, logo and asset handling, and AI-powered image generation. Your role will be to transform it into a stable, scalable, multi-tenant production platform. This includes designing scalable architecture, building backend services and AI workflows, deciding when to use deterministic code, computer vision, machine learning or generative AI, integrating multiple AI providers, and building reliable pipelines for analysis, planning, generation, evaluation and feedback.

You will also design systems for:
* Background processing
* Queues and workers
* Retry and failure recovery
* Storage and caching
* APIs
* Monitoring and observability
* Data models
* AI orchestration
* Production reliability
* Scalability and cost optimization

Building Intelligence Beyond LLMs
A major part of our vision is building our own learning layer. We do not want to depend entirely on third-party AI providers forever. The platform should collect the right data from day one, learn from approved and rejected outputs, understand client preferences, and eventually support in-house machine learning, personalization, ranking, recommendation and quality prediction systems. Our architecture should make it possible to evolve beyond external models whenever it creates real value.

Core Technical Skills. Strong hands-on experience with:
* Python
* TypeScript / JavaScript
* FastAPI (or similar)
* Node.js
* PostgreSQL
* Redis
* REST APIs
* Docker
* Linux
* Experience with AWS, GCP or Azure, production backend systems, background workers and job queues is expected.

AI, Machine Learning & Computer Vision. Strong practical experience with:
* OpenAI and other AI providers
* Multimodal AI
* Generative AI
* Structured outputs
* Embeddings
* AI evaluation

We also expect experience with real machine learning, including:
* ML pipelines
* Model training and evaluation
* PyTorch and/or scikit-learn
* Data collection and dataset design

Computer vision experience is important, including:
* OpenCV
* NumPy / Pillow
* Image segmentation
* Object detection
* Feature extraction
* Image similarity
* Vision embeddings
* Masks and bounding boxes

Experience with models such as YOLO, SAM, CLIP, Florence, GroundingDINO or similar is a strong advantage. Architecture & System Thinking

Beyond coding, we’re looking for someone who can design scalable, modular and observable systems.

You should be comfortable making decisions around:

* System boundaries
* APIs
* Data models
* AI provider abstraction
* Queues and workers
* Storage
* Evaluation pipelines
* Model versioning
* Failure recovery
* Future ML infrastructure

The platform should scale across multiple industries and evolve without becoming tightly coupled to any single AI provider.

Who We’re Looking For. We are not looking for someone whose experience is mainly:
* Basic ChatGPT integrations
* Prompt engineering
* Zapier or Make automations
* Simple API wrappers
* Frontend-only development
* Architecture consulting without implementation

We’re looking for someone who understands how to solve problems with the right technology—knowing when deterministic software is enough, when computer vision is the better solution, when machine learning is appropriate, and when generative AI actually adds value. Most importantly, we’re looking for someone who can both design the architecture and build it.

When Applying
Please tell us about the most complex AI or ML product you have personally designed and built, and clearly explain which parts you implemented yourself.

We’d also like to understand:
* How you decide between deterministic code, computer vision, machine learning and generative AI.
* How you would design a platform that isn’t dependent on a single AI provider.
* How you would build a learning and feedback loop that improves over time.
* An example of a production system that failed, what happened, and what you changed afterward.

Please include relevant GitHub repositories, live products, technical case studies or architecture examples where possible.
Generic AI-generated applications will not be considered.

Hiring Process. The first stage is a paid technical and architecture assessment, with the goal of moving into a long-term full-time Founding AI/ML Engineer role.