Top Senior Machine Learning Jobs

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.

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.

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.

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.

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.

Contribute to machine learning model design, training, and evaluation for fraud detection. Communicate findings, develop metrics, and support production ML workflows while ensuring compliance and model governance.

Improve enterprise search QA by designing and optimizing ranking features and algorithms, building experiment tooling and platforms, evaluating ranking metrics, and partnering with product, UX, and customer teams to boost answer quality across heterogeneous knowledge sources.

Lead and direct multidisciplinary teams to research, develop, test, integrate, and demonstrate advanced signal processing, optimization, and machine learning algorithms for radar, electronic warfare, and sensor systems. Oversee modeling/simulation, algorithm T&E, customer engagement, and technical briefings for defense and intelligence applications.

Lead program management for Core Algorithm Development, coordinating algorithm, data science, firmware, software, product, and validation teams to plan, validate, integrate, and release physiological algorithms while improving processes, mitigating risks, and reporting progress to executives.

The Staff Machine Learning Engineer will lead ML initiatives, develop ML systems, collaborate with cross-functional teams, and mentor junior engineers to optimize healthcare intake workflows using advanced machine learning techniques.

Lead modelling and analysis roadmap for FX products: forecast and monitor FX volatility, optimise trade execution with statistical/ML models, run customer and profitability analyses, and partner with product, trading, risk, and engineering to translate insights into trading and product decisions.

The role involves developing machine learning systems for Mars atmospheric modeling and multi-modal data fusion, contributing to autonomous science in space missions.

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.

Responsible for validating payment network models, including fraud and AML. Engage with stakeholders, improve model accuracy, and leverage innovative technology for data analysis.

Develop and deploy machine learning solutions for autonomous vehicles, mentoring engineers and collaborating on model implementations.

Lead and deliver advanced analytics and machine learning solutions to support pricing, revenue optimization, and partner relationships. Collaborate with cross-functional teams to design, validate, and implement models at scale using large transaction datasets and modern big-data tooling. Translate technical insights into actionable business recommendations.