Machine Learning Technical Lead, Artificial Intelligence (AI) Required, Work From Home

Machine Learning Technical Lead, Artificial Intelligence (AI) Required, Work From Home As Machine Learning Technical Lead, you own the execution layer of intelligence. You will translate research direction into reliable, scalable, production-grade ML systems. This role sits at the intersection of research, infrastructure, and product. You will be responsible for making models trainable, deployable, observable, and performant under real-world constraints. This position is 100% Remote. Machine Learning Technical Lead Responsibilities: - Own end-to-end ML system execution: data pipelines, training workflows, evaluation systems, inference architecture, and deployment. - Fine-tune and adapt models using state-of-the-art methods such as LoRA, QLoRA, SFT, DPO, and distillation. - Architect and operate scalable inference systems, balancing latency, cost, and reliability. - Design and maintain data systems for high-quality synthetic and real-world training data. - Implement evaluation pipelines covering performance, robustness, safety, and bias, in partnership with research leadership. - Own production deployment, including GPU optimization, memory efficiency, latency reduction, and scaling policies. - Collaborate closely with application engineering to integrate ML systems cleanly into backend, mobile, and desktop products. - Make pragmatic trade-offs and ship improvements quickly, learning from real usage. - Work under real production constraints: latency, cost, reliability, and safety Machine Learning Technical Lead Outcomes: - Research and models reliably translate into production-ready solutions with clear performance and quality targets. - ML pipelines, training loops, and inference systems are stable, efficient, and maintainable. - Production issues are detected, debugged, and resolved quickly, minimizing user impact. - Team members are supported, aligned, and able to deliver high-impact ML work with minimal friction. - Iterations on models and systems are measurable,