We’re looking for a Sr. Applied Research Scientist to lead efforts in applying reinforcement learning based techniques to improve the quality and controllability of the models that power Runway’s research and tools. This role is for someone who has a strong background building impactful and novel machine learning projects, has strong software engineering skills, and is deeply interested in seeing their research materialize in novel user interfaces for creative tasks.
Play a pivotal role in shaping and expanding the capabilities of the Kumo Graph Neural Network and Relational Deep Learning architecture. Drive innovative designs that enhance our GNN backbone and integrate cutting-edge temporal learning strategies. Bridge the gap between foundational models and GNNs, tackling diverse use cases like recommendation systems, customer retention, forecasting, and fraud detection. Leverage your expertise in machine learning and AI to solve critical challenges, creating scalable and adaptable solutions.
Focus on recommender systems modeling at the intersection of generative recommenders and foundational understanding of user taste across music and talk content formats. Collaborate with a cross-functional team to define and execute the machine learning technical strategy for the product area, building the next generation of Spotify content and user representations and the technical architecture to support it.
You will be building the core infrastructure to serve and deploy models efficiently, as well as world-class tooling that enables us to iterate on models quickly. You will be combining industry best practices and a first-principles approach to design and build ML infrastructure that will improve Figma’s design and collaboration tool.
Lead the development of early-phase AI systems to enhance user experience and develop systems to measure and improve AI performance and relevance. Direct the future of AI at Figma in collaboration with cross-functional teams of innovative software engineers, product managers, and data scientists. Break down open-ended AI problems into engineering strategies utilizing machine learning, data analysis, and experimental design.
The AI Engineering Leader has a pivotal role in our organization, responsible for guiding the team that develops enterprise-level solutions aimed at scaling and automating value-driven outcomes for our business. The leader will oversee all aspects of AI engineering, from conceptualization to deployment, ensuring the delivery of cutting-edge solutions that meet both business and technical requirements.
Be a technical leader within your team and within Spotify. Coordinate technical projects across teams. Facilitate collaboration with engineers, product owners, and designers to solve interesting problems for delivering various media. Architect, design, develop, and deploy ML models. Be a leader in Spotify’s ML community and work collaboratively and efficiently on existing platforms and systems.
Design, build, evaluate, and ship ML solutions in Spotify’s personalization products and collaborate with cross functional teams to build new product features that advance the mission to connect artists and fans. Prototype new approaches and productionize solutions at scale for our hundreds of millions of active users. Promote and role-model best practices of ML systems development throughout the organization and be part of an active group of machine learning practitioners.
You will be responsible for setting technical strategy for your team on a year-long time scale, and help your team tie it together with critical, business-impacting projects. By collaborating with product management, design & analytics, you will ensure technical sustainability, risks and trade-offs are well understood and managed. You will foster a culture of quality and ownership on your team.
The team is responsible for products, tools and services to manage and streamline all AI efforts at LivePerson. This is an opportunity to shape the way we do data science and build AI-based products. Topics include generative AI application building, LLM fine-tuning pipelines, feature extraction and storage, experiment tracking, transformation and training pipelines, deployment and large-scale serving of models. You will be providing engineering support for ML model development.