Top Machine Learning Jobs

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.

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.

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.

Develop, prototype, test, and maintain mathematical algorithms and software; analyze and interpret data using mathematical and statistical techniques; evaluate computational resources and promote appropriate mathematical tools across teams.

Lead the design, development, and deployment of advanced machine learning solutions at Optum Tech, mentoring junior engineers and collaborating across teams.

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.

Design, deploy, and lead scalable AI/ML solutions and data engineering efforts for life sciences clients. Mentor teams, ensure architecture and coding best practices, support business development, and collaborate with clients on implementation and strategy.

Intern will own and deliver a scoped engineering project on Deepgram's voice platform (ASR/TTS/LLM/API/SDK), ship reviewed code to staging/production, use AI in development workflows, improve a component measurably, and present results with documentation.

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.

Develop, evaluate, and deploy machine learning models for real trading environments; apply rigorous statistical techniques to measure performance, control overfitting, and assess model risk and interpretability; collaborate with traders and developers; maintain best practices and production-ready systems.

Develop and validate quantitative models and automated trading algorithms for securities trading. Conduct research on trading cost, liquidity, risk models, portfolio construction, and signal generation. Collaborate with stakeholders, present model results, and implement process improvements while adhering to regulatory and risk frameworks.