Top Machine Learning Jobs in Santa Cruz, CA

As a Senior Machine Learning Engineer at Concurrency, you'll design and engineer ML systems, collaborating with data science teams to solve diverse customer problems. You'll leverage your expertise in supervised, unsupervised, and deep learning, using tools like Python and SQL, while influencing project direction and designing production solutions in a cloud development environment.

The Applied Scientist will apply advancements in AI technologies such as LLMs, CV, and NLP within the estate planning sector. Responsibilities include fine-tuning and integrating AI models, building Q&A frameworks, and collaborating with engineers to enhance AI quality in production systems.

You will lead a team to innovate in natural language processing, big data, and artificial intelligence, creating technologies that change digital interaction. This role demands creativity, strong collaboration skills, and the ability to develop prototypes rapidly.

The Machine Learning Architect will design and execute machine learning projects from proof-of-concept to deployment, architecting solutions on cloud platforms such as AWS, GCP, and Azure. The role involves collaborating with business representatives, managing all project phases, and ensuring technical excellence in machine learning delivery while staying current with advancements in the field.

The Head of Applied AI will lead the Applied AI Research team in developing innovative AI technologies for the Descript product. Responsibilities include managing research projects, mentoring team members, collaborating on product integration, developing research strategies, and representing the company at events.

As a Staff Machine Learning Engineer, you will design and develop scalable ML platforms and services, define and maintain Machine Learning data pipelines, productionize models, optimize performance, automate workflows, and promote best software engineering practices. You'll collaborate with cross-functional teams to drive ML roadmap and architectural decisions while improving productivity through shared tools.

As a Machine Learning Optimization Evangelist, you will educate the ML community on optimizing their training and inference workloads. Your role includes publishing technical blog posts, contributing to documentation, speaking at conferences, producing webinars, and collaborating with strategic partners to enhance the usage of Hugging Face models on various compute platforms.