Top Machine Learning Jobs

Lead WHOOP's strategy for transforming sensor data into real-time intelligence, managing teams, delivering features, and driving technical direction in machine learning and embedded systems.

This internship focuses on researching low-latency ML inference and hardware acceleration, involving project ownership, collaboration with engineers, and presenting findings.

Review and audit machine learning models and model risk practices across the enterprise. Partner with cross-functional teams to design, train, evaluate, validate, and implement ML and NLP solutions, using tools like Python, Spark, H2O, and AWS. Translate technical findings into business recommendations and assurance for stakeholders and the Audit Committee, while researching and applying emerging Generative AI and state-of-the-art methods.

The Staff Applied AI Engineer will develop decision intelligence systems using machine learning and optimization, focusing on improving planning decisions and operational efficiency. Responsibilities include building models for revenue attribution, designing decision-support systems, and ensuring security in AI-driven capabilities.

As a Senior Machine Learning Engineer, you'll develop and optimize models for Chewy's Sponsored Ads to enhance product selection and advertising effectiveness. You will collaborate with cross-functional teams, lead model deployment, and publish research to elevate machine learning applications while mentoring junior scientists.

As a Staff Machine Learning Engineer, you'll enhance credit products through advanced modeling techniques, data analysis, and ML operations, supporting robust decision-making processes.

As a Staff Machine Learning Engineer, you will enhance credit modeling, manage full credit modeling stacks, and support innovations within regulated banking frameworks.

The Staff Machine Learning Engineer will develop machine learning models to enhance advertising strategies, improving relevance, ranking and customer engagement for Chewy's sponsored ads.

Build anomaly detection metrics and monitoring tools to identify fraud; analyze large structured and unstructured datasets to detect suspicious activity and trends; develop ML algorithms and run experiments to optimize detection; visualize and present insights; partner with data architects, engineers, and cross-functional teams to improve fraud mitigation processes.

Lead development and implementation of AI/ML solutions, transform data into insights, design and optimize models, manage projects and budgets, supervise and mentor teams, maintain quality and client relationships, and drive innovation across analytics engineering initiatives.

Lead design and delivery of AI/ML solutions, transform data into insights, manage projects and budgets, mentor teams, maintain quality and client relationships, and drive innovation across data and analytics engineering.

Lead design and implementation of AI/ML solutions, transform data into insights, manage projects and budgets, coach teams, maintain quality and client relationships, and drive technology adoption.

As a Senior Applied AI Engineer, you will develop and deploy AI/ML solutions for semiconductor processes, design software for large datasets, and drive innovative research to improve manufacturing efficiency.

The role involves developing LLM applications for automated silicon design, optimizing models, curating datasets, and collaborating with other experts to integrate LLMs into EDA workflows.

Manage a team of ML engineers to build and optimize Affirm's economic decisioning engine using advanced ML techniques. Collaborate across teams, provide technical leadership, and mentor engineers while driving a cohesive long-term ML strategy.

Use ML, LLMs, and GenAI to detect and prevent card fraud by analyzing large transaction datasets, building automation and reporting, developing POCs, and delivering actionable recommendations to cross-functional partners to optimize risk and customer experience.

Build AI/ML-driven analytics and scalable data architectures to process large transaction datasets, automate manual processes, and develop models (including sequential decision tools) that optimize balance sheet and deposit management. Lead projects end-to-end, present commercial recommendations, and collaborate with Quant Research, Treasury, CIO, Technology and CDAO stakeholders.

Apply advanced quantitative and computational methods to build and deploy machine learning, optimization, and statistical models on large structured and unstructured consumer data. Drive full modeling lifecycle from problem definition to communication of results and recommendations, present findings to senior stakeholders, and act as a subject-matter expert while contributing new ideas to the team.

Build and validate ML, optimization, and statistical models on large structured and unstructured consumer datasets; generate insights and actionable recommendations; communicate results to senior stakeholders; contribute across the full modeling lifecycle and advance team knowledge.

Lead quantitative modeling for CIB Treasury, developing and scaling statistical and ML models (including commercial real estate and deposit modeling), manage model lifecycle and documentation, build cloud-based data solutions, and present model-driven recommendations to stakeholders.

The Staff ML Engineer will design and operate Samsara's ML platform, collaborating with teams to enhance ML features and improve safety outcomes. Responsibilities include overseeing system reliability, leading technical direction, and mentoring engineers.

Lead the development of machine learning systems for Cedar Pay's Personalization Engine, manage ML engineers, and ensure ML model effectiveness.

Develop and scale machine learning systems to enable AI coaching, collaborate across teams for member insights, and contribute to architectural decisions.

The Senior Machine Learning Engineer designs and develops ML systems, collaborates with teams to meet analytical needs, and optimizes large-scale ML solutions while setting monitoring and best practices.

Lead Gusto's AI/ML strategy, managing teams in Machine Learning Engineering, AI, and Risk Data Science. Ensure systems are reliable and impactful while fostering a culture of technical excellence.