Machine Learning Engineer (Computer Vision + Full Stack)

Company Description

VN is the foundational neutral infrastructure connecting the Artificial Intelligence Intellectual Property (AI IP) ecosystem. Our mission is to address the critical challenge of tracking, attributing, and monetizing copyrighted content in the rapidly evolving generative AI space. By combining forensic proof with authorized licensing frameworks, we empower creators and rights holders to thrive in this new era of AI innovation. Founded on the principles of protecting and enhancing creators' rights, VN enables a future where AI becomes an opportunity instead of a threat. Join us in revolutionizing the way AI interacts with intellectual property by visiting vn.ai and learning about our mission.

Please see the role description below and apply on Wellfound.

About the Role

We are looking for a highly skilled Machine Learning Engineer with strong experience in computer vision, multimodal models, and high-scale ML systems to help us build an automated system for detecting copyright infringement at internet scale. As our first engineering hire, you’ll own the end-to-end ML lifecycle—from experimentation and fine-tuning to production deployment—and you’ll also jump into full-stack development when needed to help integrate models into our product.

This is a high-impact, hands-on role in a fast-moving startup. You should be excited about working autonomously, solving ambiguous problems, and wearing multiple hats across ML, MLOps, backend, and occasionally frontend.

Responsibilities

Machine Learning / Data Science

• Develop and fine-tune computer vision and multimodal models for identifying copyright infringement and similarity detection at scale.
• Build and optimize pipelines for embeddings, vector databases, and retrieval-augmented generation (RAG).
• Architect scalable systems for training, inference, and continuous improvement.
• Design experiments, evaluate model performance, and implement improvements across datasets and algorithms.
• Work with embedding models, CLIP-style architectures, and multimodal LLMs.

MLOps / Systems Architecture

• Own end-to-end ML infrastructure from prototyping to production.
• Deploy and manage large-scale training and inference systems on AWS, GCP, or similar platforms.
• Build automated data pipelines, monitoring, and CI/CD workflows for ML models.
• Ensure reliability, scalability, and observability of distributed systems.

Software Engineering / Full-Stack

• Build backend APIs and services to integrate ML models into the product (Python, Node/JS).
• Collaborate with product and engineering to ship features end-to-end.
• Work with modern web stacks (Next.js, React) when integrating models into UI flows.
• Contribute to core platform architecture and technical roadmap.

Startup / Cross-Functional

• Work directly with founders in a fast-paced, experimental environment.
• Own major technical decisions and help define the overall engineering culture.
• Be resourceful—prototype quickly, iterate often, and think in terms of business value.
• Wear multiple hats across product, data, architecture, and execution.

Requirements

Technical

• Strong experience with computer vision, deep learning, and multimodal models.
• Proficiency with Python and familiarity with JavaScript/TypeScript (Node, Next.js).
• Hands-on experience with MLOps: model deployment, CI/CD, monitoring, GPU management.
• Experience with AWS or GCP (SageMaker, Lambda, GKE, Cloud Run, S3/GCS, etc.).
• Solid understanding of vector databases, embeddings, RAG, and large-scale retrieval systems.
• Experience building and maintaining production-grade APIs and microservices.
• Strong systems-engineering mindset: scalability, optimization, distributed systems.

Preferred

• Experience with image similarity search, hashing, or watermark detection.
• Experience with fine-tuning foundation models (Vision Transformers, CLIP, LLaVA, etc.).
• Exposure to large-scale data processing (Spark, Ray, Dask, etc.).
• Prior startup experience or desire to work in a deeply hands-on, zero-to-one environment.

You are a good fit if you:

• Want to be a founding engineer with major ownership.
• Enjoy switching between ML research, backend engineering, and product implementation.
• Are excited by building systems that operate at internet scale.
• Take pride in shipping fast, iterating often, and working with ambiguity.
• Want to help shape the future of AI-powered content protection.

Compensation

• Competitive salary + meaningful equity.
• Flexible, founder-level impact.