Staff Machine Learning Engineer, Artificial Intelligence (AI) Required, Work From Home   As the Staff Machine Learning Engineer, you own the execution layer of intelligence.  You translate research direction into reliable, scalable, production-grade Machine Learning (ML) systems.  This role sits at the intersection of research, infrastructure, and product. You are responsible for making models trainable, deployable, observable, and performant under real-world constraints.  This position is 100% Remote.   Staff Machine Learning Engineer Responsibilities:   - Own end-to-end Machine Learning (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 Machine Learning (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   Staff Machine Learning Engineer Outcomes   - Research and models reliably translate into production-ready solutions with clear performance and quality targets. - Machine Learning (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 Machine Learning (ML) work with minimal friction. - Iterations on models and systems are measurable, safe, and improve user experience over time.