Top Machine Learning Jobs

As a Machine Learning Engineer, you will fine-tune transformer models, deploy machine learning solutions, and work with cross-functional teams on innovative projects.

The Machine Learning Engineer will develop and maintain scalable ML systems, collaborate across teams, and innovate solutions for real-time applications to enhance customer experiences.

Lead end-to-end ML model development for safety-critical driving behaviors, design experiments, improve data quality, deploy models and metrics fleet-wide, and communicate results across product and engineering teams.

Design and build reliable, scalable data analysis libraries, applications, and pipelines; collaborate on architecture and code reviews; prototype ML solutions; own full software development lifecycle; dive into engineering problems with domain experts to support launches and Starlink operations.

Design, train, test, deploy, and maintain machine learning models for DoD missions. Collaborate with data engineers, scientists, and product owners to implement LLMs, agentic AI, computer vision, and generative AI solutions, and guide clients on ML tools and frameworks.

Own and improve production-scale ML models and systems for Spotify Home feed and Shortcuts. Build personalized recommendations and LLM-powered experiences, train and fine-tune LLMs, run A/B tests, optimize inference and cost, and improve ML platform, data pipelines, and production infrastructure at scale.

Design, build, and deliver ML components and pipelines: feature engineering, model training/evaluation, deployment as services or batch/streaming jobs, production monitoring, and cross-functional collaboration while following governance and documentation standards.

Design, train, test, deploy, monitor, and maintain ML models (including LLMs/generative AI) for national security missions. Collaborate with data engineers, software developers, data scientists, and stakeholders to apply ML algorithms, triage large datasets, and deliver production-safe, mission-critical AI/ML solutions.

Lead end-to-end development of detection algorithms and production ML/physics/LLM models for HVAC sensor time-series. Build evaluation harnesses, label fault sets, optimize data pipelines, and translate model outputs into actionable field insights while partnering with hardware, software, and business teams to ship reliable customer-facing features.

Design and implement scalable AI/ML-driven HealthTech microservices, contribute to technical architecture, write high-quality code, perform code reviews, develop proofs-of-concept, troubleshoot production issues, and document work while collaborating in agile teams.

Design, implement, and integrate machine learning models and data pipelines for a programmatic DSP. Support model development for prediction, forecasting, and fraud detection; prepare large datasets; collaborate with cross-functional teams; document experiments and maintain reproducibility.

Lead design and implementation of production ML systems for low-latency ad use cases (bidding, ranking, pacing, fraud). Build training and feature pipelines, real-time inference services, and ML team. Partner with engineering and product, own model experimentation, evaluation, monitoring, and deployment at scale.

Design, architect, develop, integrate, and support production ML solutions on AWS. Collaborate with product and domain experts, mentor junior engineers, guide feature implementation, and use metrics to drive decisions while writing production-quality code.

Research-focused ML intern to design and run forecasting and anomaly/risk prediction experiments, develop time-series and probabilistic models, improve feature engineering and dataset quality, build reproducible research workflows, and communicate technical findings.

Build, optimize, and deploy production-grade machine learning systems for sports analytics. Improve data pipelines, feature engineering, model training, evaluation, and low-latency prediction services. Collaborate with DevOps and Data Engineering to scale workloads on Kubernetes, maintain cloud-native EDW/ETL solutions, and promote software development best practices.

Design, build, and deploy machine learning models for a payments platform, overseeing end-to-end solutions and experimentation, while monitoring models in production.

The role involves developing scalable ML algorithms, optimizing LLM inference performance, managing model serving pipelines, and collaborating with data engineers for efficient knowledge graph generation.

This role involves bridging research and applied engineering, overseeing training infrastructure, optimizing workflows, and setting technical strategies for AI/ML deployment at zaimler.

The Staff ML/AI Platform Engineer will design, prototype and deploy AI/ML models, optimize pipelines, mentor junior engineers, and collaborate closely with other teams to develop machine learning solutions.

The Machine Learning Engineer will build tools for interpretable AI, optimize infrastructure, and integrate workflows into products, ensuring reliability and performance.

The Machine Learning Engineer will implement and evaluate ML algorithms, focusing on model development, A/B testing, and cross-functional collaboration to drive innovative solutions for financial challenges.

As a Machine Learning Engineer at FanDuel, you'll develop scalable ML applications, enhance user experience through intelligent systems, and collaborate across teams to validate AI insights.

The Machine Learning Engineer will design and implement search systems, develop scalable serving systems for ML, and collaborate with teams to productionize AI insights, requiring engagement with big data and machine learning frameworks.

As a Staff ML Engineer, you'll lead complex model quality improvements in clinical AI, spanning research and engineering, optimizing performance under high compliance conditions.

The Machine Learning Engineer will design and implement advanced ML models for energy systems, collaborating with R&D teams to tackle challenges in battery technology.