ML Engineer

ML Engineer

Salary: Competitive, based on experience
Work: Remote

Company Description

Qoves is on track to become the next consumer tech unicorn. With over 2.5 million followers across our platforms, we have built one of the largest global communities around beauty, aesthetics and AI. In only a short time, we have become one of the fastest growing consumer tech companies in the world, pioneering AI-driven facial analysis that is transforming the way people understand and improve their looks.

Our products range from consumer facial analysis reports to partnerships with leading clinics. We are scaling quickly, expanding our team and setting new standards in how technology and aesthetics intersect. This is your opportunity to join a company that is not just growing but reshaping an entire industry.

Role Description

We're looking for an experienced ML Engineer to join our team full-time on a remote basis. You'll work at the core of our AI-powered facial analysis platform - building, training, and deploying the models that drive our product. You'll report directly to our CTO/Founder and operate with a high degree of autonomy.

Key Responsibilities

• Design, train, and deploy machine learning models for facial analysis, image generation, and fine-tuned image editing
• Translate findings from research papers into practical, production-ready engineering solutions
• Build and improve generative models (GANs and diffusion-based architectures) for realistic image synthesis and manipulation
• Develop and refine facial landmarking models for precise feature detection and measurement
• Own the full ML lifecycle from data collection and annotation strategy through to model evaluation and deployment
• Coordinate with annotation teams to define labelling standards, manage data quality, filter datasets, and engineer training data pipelines
• Experiment with and benchmark new model architectures, loss functions, and training strategies
• Optimise model performance for inference speed and accuracy in production environments
• Collaborate with backend engineers to integrate models into the platform's API layer
• Stay current with the latest research in computer vision, generative AI, and facial analysis

Required Qualifications

• 5+ years of professional ML engineering experience at an established company (not solely academic or freelance)
• Strong proficiency with PyTorch as a primary deep learning framework
• Demonstrated ability to read, understand, and implement techniques from academic research papers into working systems
• Experience working independently and managing end-to-end ML workflows with minimal supervision
• Hands-on experience coordinating with data annotation teams - defining annotation guidelines, quality control, dataset filtering, and data engineering
• Solid understanding of computer vision fundamentals - CNNs, image segmentation, object detection, feature extraction
• Experience training and evaluating models at scale, including hyperparameter tuning and experiment tracking
• Strong Python skills and comfort working in production codebases

Preferred Qualifications

• Experience with Generative Adversarial Networks (GANs) for image generation and fine-tuned editing
• Experience with facial landmarking models and facial feature detection
• Experience with image editing and manipulation models (inpainting, style transfer, image-to-image translation)
• Published research in computer vision, generative AI, or related fields
• Familiarity with diffusion models (Stable Diffusion, DDPM, etc.)
• Experience with model serving and optimisation (ONNX, TensorRT, or similar)
• Experience with cloud GPU infrastructure (AWS, GCP)

Why Work at Qoves

• Build ML models that directly power a product used by millions
• Well-funded startup with 2.5M+ community and strong market traction
• High autonomy and ownership over model architecture and research direction
• Work directly with the CTO/Founder in a meritocratic, mission-driven culture
• Shape the future of an entire industry at the intersection of AI and aesthetics