Senior Machine Learning Engineer

4+ years of hands-on experience in machine learning, data science, or related fields, with a strong emphasis on practical engineering applications and deploying robust solutions into production environments.
Extensive experience with large language models (LLMs), including fine-tuning, prompt engineering, Retrieval-Augmented Generation (RAG), and deploying LLMs in user-facing products.
Experience designing, developing, and deploying end-to-end machine learning systems, including data pipelines, model training and serving, and monitoring solutions.
Demonstrated ability to guide projects and foster a collaborative and high-performing work environment that values continuous improvement and knowledge sharing.
Ability to break down complex projects, effectively scope and sequence work, and manage timelines to ensure the timely and successful delivery of machine learning initiatives.
Excellent verbal and written communication skills, with the ability to convey complex technical concepts to both technical and non-technical stakeholders.

Lead the development, design, and maintenance of cutting-edge LLM-based applications, ensuring the translation of generative AI research into production-ready technologies.
Lead prompt engineering initiatives for LLM-based applications, working closely with product managers and non-technical stakeholders to optimize model performance and meet quality benchmarks.
Design and deploy orchestration and evaluation frameworks to ensure high-quality, low-latency generative AI solutions, optimizing performance and reliability for large language models.
Design and develop scalable, efficient machine learning training and inference pipelines, ensuring seamless integration with existing production systems and adherence to best engineering practices.
Support operational excellence by participating in initiatives to streamline machine learning workflows and establish standardized procedures to ensure consistent and high-quality outcomes across our ML projects and systems.
Collaborate closely with ML engineers and cross-functional teams, including data scientists, software engineers, product managers, and business stakeholders, to foster a culture of collaboration, scientific inquiry, continuous learning, and drive the successful implementation of machine learning projects.
Employ a diverse set of tools and platforms, including Python, AWS, Databricks, Docker, Kubernetes, FastAPI, Terraform, Snowflake, and GitHub, to develop, deploy, and maintain scalable and robust machine learning systems.

Advanced degree (Master’s or Ph.D.) in Computer Science, Statistics, Data Science, or a related technical field is preferred.
A sense of humor is optional but appreciated.