Fine-tune state-of-the-art models, design evaluation frameworks, and bring AI features into production. You will fine-tune and adapt open-source models, engineer prompts and curate data, evaluate models, deploy and monitor models, and collaborate with product, engineering, and design teams to launch user-facing AI features.
Drive applied research into LLMs and agentic systems, rapidly prototyping, validating, and shipping production-ready capabilities. Stay hands-on with the latest advances in foundation models, LLM-as-a-judge, agentic frameworks & tools, and move fast to bring them into the product. Leverage emerging technologies and a team of legal experts to improve the intelligence and autonomy of our agents.
Focus on making cutting-edge vision models production-ready in an execution-focused engineering role. You'll work across our training and inference stack to deploy scalable, high-performance machine learning services that power real-world applications for thousands of developers, integrating new model capabilities, optimizing inference performance, and ensuring robust, maintainable systems.
The Staff Machine Learning Engineer will own the design, development, and scaling of ML systems powering next-generation payment flows, recommendation engines, and agentic commerce features; architect and scale ML systems powering agentic commerce workflows, including autonomous decision-making and checkout automation.
Implement state-of-the-art models and techniques, including deep learning architectures and foundation models. Diagnose model performance issues, analyze sources of error and design strategies to mitigate them. Work with scientific and biological data to contribute to projects in drug discovery, life sciences and healthcare. Design and run experiments, critically evaluate results and communicate findings through clear, data-driven reports.
The Senior Machine Learning Developer will design and implement intelligent personalization systems for workplace automation and user experience enhancement, participate in ML architecture design, performance optimization, and end-to-end MLOps automation with a focus on contextual recommendations, experience personalization and behavioral pattern recognition.
Design, build, and maintain large-scale distributed training infrastructure for Ads ML models. Develop tools and frameworks on top of the Ray platform. Build tools to debug, profile, and tune distributed training jobs for performance and reliability. Integrate with object storage systems and improve data access patterns. Collaborate with ML engineers to improve model training time, efficiency, and GPU training costs. Drive improvements in scheduling, state management, and fault tolerance.
In this role as Sr. Staff Machine Learning Engineer, you will define and lead the architecture and development of large-scale, foundational machine learning systems that power care personalization, treatment intelligence, and automation at scale. Your work will directly shape MedMatch—our AI-powered system—by owning the architecture and direction of key ML components, enhancing provider-patient interactions, optimizing treatment recommendations, and expanding into new verticals.
In this role as Principal Machine Learning Engineer, you will define and drive the architectural vision for large-scale, foundational machine learning models and applications that power care personalization, treatment intelligence, and automation at scale. This impact will be driven by architecting resilient, scalable, and extensible ML platforms that accelerate product development, unlock intelligent care delivery, and influence long-term ML strategy and engineering practices across teams.
As a Senior Machine Learning Engineer at Artera, you'll be a key technical leader driving the development of AI-based biomarkers that personalize cancer therapy. You will design, build, deploy, and continuously improve multimodal models—combining whole-slide images with clinical and molecular data—to predict molecular traits and patient outcomes. You’ll translate clinical needs into robust, well-validated solutions.