Top Machine Learning Engineer Jobs

The role entails developing AI/ML applications, collaborating across teams, optimizing code, and mentoring juniors while focusing on user experience.

The System Engineer will test Machine Learning models, optimize system performance, and automate data set characterization while ensuring integration with current products.

As a Principal Software Engineer, you will lead backend development for a cloud security product, focusing on machine learning integration, performance optimization, and collaboration across teams to enhance product effectiveness.

Develop and optimize ML compilers for high-performance deep learning applications, collaborating with hardware engineers to enhance performance and efficiency while minimizing energy consumption.

The Staff Software Engineer will lead the design and implementation of scalable backend services for Machine Learning models, focusing on system reliability and mentoring other engineers.

The Senior Software Engineer, AI/ML will develop scalable data streaming solutions, integrate AI capabilities, collaborate with teams, and mentor junior engineers.

The ML Research Engineer will optimize training frameworks, post-train models, collaborate on AI development, and create algorithms for agent training.

Lead the architecture for ML models on custom silicon, influencing AI frameworks and solving performance problems across the software stack.

The role involves building and operating ML infrastructure for large-scale training, managing data pipelines, and supporting model deployment while collaborating with various teams.

As a Senior Machine Learning Engineer, you will enhance diffusion models, conduct research, collaborate on system implementations, and communicate findings effectively.

The Principal Compiler Engineer will lead compiler engineering, optimizing ML model performance and coordinating with development teams to innovate compiler infrastructure.

The Staff ML Ops Engineer builds and maintains infrastructure for ML applications, ensuring they are robust and production-ready, while collaborating with data scientists and ML engineers.

The Staff Software Engineer, AI/ML will design and develop intelligent agents, integrate them with existing systems, optimize performance, and collaborate with product teams, focusing on innovative AI technologies.

The role involves improving the data platform for Perception model training, developing APIs, and supporting cross-team integration while mentoring junior engineers.

As Lead Machine Learning Engineer, you will develop and deploy scalable ML models, optimize search relevance, and enhance user experience in an online marketplace environment.

Design, build, and improve ML systems and infrastructure, mentor junior engineers, and write maintainable code in a customer-focused environment.

Develop technical solutions for assortment decision-making, implement MLOps best practices, and collaborate on advanced analytical methods within the SAS team at 84.51°.

The Senior Software Engineer will build scalable systems for ML model training and deployment, collaborating with researchers and engineers to improve workflows and ensure efficiency.

The Staff Machine Learning Engineer will lead the development of product personalization using advanced machine learning techniques. They will mentor team members, collaborate with cross-functional partners, and shape the machine learning vision at StubHub while ensuring impactful deployment of algorithms.

Lead the vision and execution strategy for ML & AI personalization at StubHub, mentoring a team of Machine Learning Engineers and collaborating with cross-functional stakeholders.

As a Machine Learning Engineer, design imaging algorithms, build acoustic simulations, and develop computer vision systems to enhance brain imaging technology.

The Senior Software Engineer will develop algorithms for processing video and sensor data, improve ML models, and collaborate on backend systems and data infrastructure.

The role involves designing and maintaining large-scale ML infrastructure, optimizing distributed training systems, and enhancing computing performance for model training.

Collaborate with engineering teams to implement machine learning solutions, deliver proofs-of-concept, and ensure customer adoption of AI-driven workflows.

Build and optimize infrastructure for private, personal AI models, ensuring performance and privacy while deploying at scale.