Top Machine Learning Jobs

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 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.

The role involves enhancing a search-based question-answering platform through ranking features, algorithm frameworks, and performance metrics while collaborating with various teams to solve engineering challenges.

Develop and maintain probabilistic pricing models for real-time sports betting. Analyze large sports datasets, apply machine learning and statistical techniques, optimize model accuracy and computational performance, debug and document code, use source control, and collaborate with traders, data scientists, and developers to productionize pricing algorithms.

The AI Solutions Architect develops ML solutions, integrates them into systems, collaborates with stakeholders, and reviews code while maintaining technical architecture.

Design, implement, and productionize scalable ML models and microservices. Build and maintain large-scale data pipelines, prototype solutions, participate in design reviews, and collaborate with product and engineering teams to deliver ML-driven product features.

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.

Partner with healthcare clients to discover, design, and deliver production-grade AI/LLM solutions. Architect cloud-native systems, build and deploy ML/LLM applications, prototype rapidly, troubleshoot distributed systems, and lead client engagements to drive measurable business outcomes while collaborating across product, platform, and engineering teams.

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.

Develop and deploy machine learning systems for Mars missions, enhance atmospheric modeling, and integrate multi-modal data for autonomous science.

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

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.

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

Develop, validate, and interpret ML models and end-to-end pipelines to predict oral drug absorption using large, heterogeneous biopharmaceutics and clinical datasets. Emphasize model explainability, uncertainty quantification, and mechanistic/physics-informed approaches, translate outputs into actionable insights for drug development teams, and communicate results via reports, presentations, and publications while contributing to AI-enabled predictive platforms.

Partner with senior data scientists and engineers to explore large datasets, prototype and validate interpretable ML models, support deployment and monitoring, translate business requirements into analytics, build dashboards and visualizations, and adopt best practices for model development, testing, and productionization to support Corporate & Institutional Banking decisions.

Design, implement, and productize scalable ML models and services. Build and maintain large-scale data pipelines and microservices. Collaborate with product and engineering teams, prototype solutions, lead design reviews, mentor junior engineers, and stay current with ML research to improve customer-facing features and platform capabilities.

As a Principal Machine Learning Systems Engineer, you will lead the development and implementation of machine learning systems and solutions to enhance team collaboration and productivity across Atlassian's software products.

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.

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

The role involves deploying machine learning to enhance Chewy's advertising solutions, developing models for product ranking and prediction, and collaborating with product leaders. Responsibilities include leading new models, publishing research, mentoring, and driving technical strategies.

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

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.

Lead the development of fraud prediction models using machine learning, collaborating across teams to build, scale, and monitor models in production.

Lead a team of ML engineers to develop fraud detection models, defining strategies, driving model evolution, and ensuring effective integration with cross-functional teams.

Lead quantitative model risk assessments and develop alternative modeling approaches for loan loss forecasting and ACL. Apply econometric, statistical, and machine learning methods, maintain model accuracy, produce transparent documentation, and communicate technical results to management, regulators, and stakeholders. Leverage cloud and open-source tools, collaborate with model development and risk teams, and drive continuous improvement in modeling practices.