Machine Learning Engineer – Contract/Freelance – Remote - $7000 USD per month

Fully Remote from anywhere in South America

12 month contract

$7000 USD per month

Responsibilities

• Partner with product and engineering stakeholders to define the AI/ML roadmap aligned with business goals.
• Design, prototype, and deploy production-ready ML models, with a focus on computer vision and multimodal understanding in the video domain.
• Apply both generative and discriminative machine learning techniques to real-world problems in the video content space
• Work on task types including text-to-image generation, image segmentation, and object and pose detection — with opportunities to leverage and deploy public-domain models for speech recognition and audio intelligence.
• Own the full ML lifecycle, from data acquisition and preprocessing to training, evaluation, and deployment
• Design and implement efficient, lightweight ML models optimized for deployment on mobile and edge devices.
• Collaborate with engineering teams to integrate ML models into the partner’s platform
• Develop LLM-based Agent functionality with custom tools and workflows
• Optimize models for real-time inference and performance on mobile device and cloud platforms
• Conduct code reviews, promote engineering best practices, and mentor junior team members
• Stay up-to-date with the latest research, tools, and industry trends in AI/ML and assess their relevance for our growth partner’s product

Experience & Qualifications

• Experience in applied machine learning, with a strong track record of building and deploying models in production
• Proficiency in Python and common ML frameworks (e.g., PyTorch, TensorFlow, scikit-learn)
• Experience with both generative (e.g., diffusion, transformer-based) and discriminative (e.g., classification, segmentation) AI approaches
• Strong understanding of data pipelines, model evaluation, and feature engineering
• Experience with computer vision and/or audio processing techniques is a strong advantage
• Familiarity with deploying ML models on cloud platforms (e.g., GCP, AWS) and edge/mobile environments (e.g., ONNX, TensorFlow Lite)
• Advanced English