Location: Full remote.
Schedule: Full time
As a Machine Learning Engineer, you will drive initiatives from concept to implementation, working at the intersection of computer vision, audio intelligence, personalization, and predictive analytics to shape the future of livestreaming. You will design and implement robust ML solutions, connecting complex data-driven insights to tangible product improvements and user value. With a strong focus on innovation, experimentation, and continuous learning, the role also demands effective collaboration within our growth partner’s cross-functional teams, ensuring clear communication and shared ownership of outcomes.
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
5+ years of 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