Remote Software engineering Jobs · LLM

Lead and mentor a team of AI engineers, providing coaching and performance feedback. Oversee the delivery of AI-powered product features, from prototypes to scalable production systems. Collaborate with Product, Design, and Data teams to ensure AI solutions are impactful and aligned with business goals.

• Explore and preprocess raw, messy datasets and design data strategies.
• Prototype model ideas and translate prototypes into production.
• Collaborate with cross-functional teams to turn ideas into impactful features.

• Design, implement, and deploy AI-powered features, including model training, fine-tuning, and prompt engineering workflows.
• Translate product requirements into robust, production-ready AI solutions, working with Product Managers, Software Engineers, and Data Scientists.
• Optimize models and infrastructure for scalability, latency, and cost efficiency, partnering with DevOps and MLOps to ensure reliable and maintainable AI pipelines.

• Lead a cross-functional team of ML engineers, data scientists, and MLOps professionals.
• Oversee the full lifecycle of LLM and ML projects — from data collection to training, evaluation, and deployment.
• Provide technical direction on large-scale model training, fine-tuning, and distributed systems design.

Lead AI and ML initiatives to design and implement production-grade machine learning systems and pipelines. Develop scalable infrastructure for model training, evaluation, and deployment, ensuring reliability and observability. Collaborate with cross-functional teams to drive innovation and efficiency.

• Lead AI engineering execution across the build organization.
• Partner with the Director of Product - AI.
• Define how millions of people experience automation in the AI era.

• Build the shared AI execution platform that powers every AI product.
• Shape the unified architecture, guide tough tradeoffs, and elevate engineering standards.
• Ensure the platform is scalable, safe, cost-efficient, and easy to build on.

• Lead AI discovery sessions and technical assessments with clients, developing prototypes and proof-of-concepts for real-world applications.
• Guide internal AI product development, evaluating and integrating emerging tools and frameworks into delivery methodologies.
• Author technical content such as white papers, case studies, and blog posts, and deliver presentations at conferences or webinars.

• Build and design end-to-end training pipelines for AI models, covering data ingestion to inference.
• Architect scalable inference systems using tools like vLLM, TensorRT-LLM, or DeepSpeed.
• Shape early product direction by experimenting with new use cases and building AI-powered experiences.

• Support the development and improvement of engineering infrastructure for a novel agentic AI system.
• Contribute to the development of tools and frameworks to assess and mitigate AI-related risks in agentic AI.
• Support technical experiments that conduct evaluations of LLMs and AI agents with engineering infrastructure.