Top Machine Learning Jobs

Design, implement, and deploy machine learning models and systems for recommendations and related problems. Collaborate with engineers, data scientists, and product partners to analyze data, build proofs-of-concept, develop ML/optimization models, write production-quality code, and evaluate models against business goals.

Design and optimize machine learning algorithms for sectors like energy and healthcare. Conduct simulations, validate performance, and collaborate with teams.

The role involves designing and maintaining machine learning models, analyzing data, and optimizing algorithms to solve business problems.

Design and implement machine learning models to solve business problems, analyze data for insights, and optimize model performance.

Design and execute benchmarks, curate datasets, evaluate benchmarks, and build evaluation infrastructure for AI model assessment while collaborating with R&D.

The AI/ML Engineer will design and implement AI/ML models to enhance analytics for government decision-making, ensuring accuracy, security, and alignment with objectives while mentoring staff.

The Software Engineer, Applied AI will develop machine learning solutions and classification models, enhancing text, image, and video understanding on a policy intelligence platform.

The role involves collaborating across departments to build AI-driven models, optimizing data for decision support, and developing scalable solutions for business growth.

Lead design, architecture, and deployment of end-to-end ML solutions for onboard autonomous driving. Apply foundation-model alignment, unsupervised pre-training, imitation and reinforcement learning, and scale large-model training pipelines. Collaborate cross-functionally, mentor engineers, conduct research, and productionize foundation models for safe, reliable real-world driving.

Lead the development of AI/ML systems in robotics and manufacturing, designing ML architectures, and collaborating with cross-functional teams.

The Lead Machine Learning Engineer will design, build, and deploy machine learning models and data pipelines, ensuring high performance and governance of ML applications while collaborating in an Agile environment.

Drive technical strategy for the Personalization Platform, develop recommendation systems, maintain ML workflows, and innovate AI solutions for customer engagement.

Leads machine learning development and programming, mentors juniors, optimizes data pipelines, and collaborates with stakeholders to inform business decisions.

As a Machine Learning Engineer at Doppel, you will design, train, and deploy ML models for detecting malicious content, ensuring scalability and real-time performance while collaborating with teams and stakeholders.

This role involves leading the development of AI systems to replace lab toxicity experiments, focusing on end-to-end ML architecture, research on chemistry-biology correlations, and building innovative models. Candidates should have strong ML skills, engineering abilities in Pytorch and Python, and a passion for both science and technology.

The role involves building and improving systems for training AI models, focusing on algorithms and infrastructure for reinforcement learning. Responsibilities include enhancing performance, reliability, and usability of systems to support research.

Lead design, build, and deployment of scalable ML/AI models (including image/vision) to production. Collaborate with engineering and business teams, monitor model quality and bias, and apply data-driven solutions to predict and aggregate risk.

As the Area Technical Lead, you will architect and improve the ML data strategy for autonomous driving, enhancing the data-driven self-improvement process for the Waymo Driver.

The role requires a skilled Machine Learning Engineer to create complex models and AI solutions while collaborating with teams to enhance products and services.

Lead and support a team of engineers to build recommendation systems, guiding technical direction and collaboration for impactful music discovery at Spotify.

As a Machine Learning Engineer, you will design and deploy models, collaborate with teams to solve business challenges, optimize performance, and maintain model effectiveness.

The Machine Learning Engineer will lead compliance efforts, review data sets, create databases, perform gap analyses, and evaluate existing AI systems, requiring collaboration with various stakeholders.

Lead development and deployment of advanced AI/ML models for sustainability and energy transition projects. Collaborate with domain experts to define AI-ready problems, manage end-to-end data and modeling pipelines, integrate solutions into digital twins and operations, mentor team members, publish and patent findings, and drive internal and external collaborative research while maintaining lab safety.

The Applied Scientist will drive research on efficient adaptive machine learning, deploy ML systems end-to-end, and contribute to product strategy, focusing on real-world impact and innovation.

The ML Engineer will design, develop, and deploy AI-native ML systems for demand forecasting and labor optimization, owning the entire ML lifecycle from data ingestion to monitoring.