Senior Machine Learning Engineer Jobs in TieTalent San Francisco, CA

Responsibilities:

• You will collaborate with an accomplished and motivated team of individuals to deliver high-impact solutions for our users and our business
• 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
• Demonstrated ability to guide projects and foster a collaborative and high-performing work environment that values continuous improvement and knowledge sharing
  
  

Qualifications:

• 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
• 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
• A sense of humor is optional but appreciated
• Python
• AWS
• Docker
• Kubernetes
• Terraform
• Github
• Machine Learning
• Prompt Engineering