Top Machine Learning Jobs

The Machine Learning Researcher will develop AI agents for private equity-backed workflows, focusing on ML cycle efficiency and decision-making.

As an AI/ML Engineer, you will develop and optimize machine learning models, collaborate with teams, and enhance mobile app features using AI techniques.

The Staff Machine Learning Engineer (Pricing) will design and deploy ML systems for pricing optimization at GoFundMe, including model development, real-time inference, and metrics evaluation while collaborating with cross-functional teams.

Four-month, full-time research fellowship focused on ML systems and performance. Fellows build and evaluate infrastructure-heavy projects (e.g., CPU simulators, accelerator backends, large-scale distributed training), use external models/APIs, receive mentorship, funding for compute, and aim to produce public research outputs.

Lead the design, development, and implementation of advanced machine learning models, collaborating with teams to enhance services through AI/ML solutions.

The role involves designing and optimizing recommendation systems for Fortnite's Discover experience, including model implementation and A/B testing.

Lead end-to-end ML projects for fraud detection: develop, deploy, monitor, and iterate production models and experimentation frameworks. Improve model performance, build scalable ML pipelines, collaborate with Product/Engineering/Risk, and champion testing, documentation, and model-monitoring best practices to ensure robust, high-impact solutions.

The Senior Machine Learning Engineer will design and deploy AI systems, optimize production models, and ensure efficient performance in resource-constrained environments.

The Machine Learning Engineer will develop software solutions, optimize data pipelines, and deploy machine learning models using cloud platforms and best practices.

The Staff Software Engineer will build and scale machine learning systems for personalization, collaborating with teams to enhance project quality and performance.

The Machine Learning Researcher at Virtu will develop and implement innovative deep learning models to solve complex market problems, collaborating with cross-functional teams and directly impacting business outcomes.

Lead ML engineering and infrastructure teams to design, build, and scale end-to-end ML systems (recommendation, personalization, ads), architect distributed low-latency serving and streaming platforms, oversee ML pipelines, ensure reliability, observability, cost efficiency, and partner with product and engineering to deliver ML-driven experiences.

Design, implement, deploy, and monitor production-grade ML and computer vision models (CNNs, transformers, vision-language). Evaluate models, analyze data, improve robustness, support MLOps/CI-CD, and mentor junior engineers within an Agile team.

Design, build, and ship end-to-end personalization, ranking, and recommendation systems; prototype and scale new ML capabilities (including LLM fine-tuning and agentic workflows); partner with product and engineering to define strategy and deploy models to production at scale; lead architecture and code reviews, mentor engineers, and drive technical direction in a regulated fintech environment.

Develop and prototype AI/ML-driven automation for ASIC/SoC design and implementation. Collaborate with academia and cross-functional teams to apply GenAI, RL, GNNs, CNNs, and other ML methods to VLSI CAD flows, pilot solutions on real designs, and drive tool/methodology improvements for power, performance, area, and quality.

Product-oriented senior ML role to build, deploy, and operate models and ML systems (classification, extraction, entity resolution, clustering, ranking, anomaly detection, forecasting, LLM pipelines). Define datasets, labeling, evaluations, monitoring, and collaborate with engineering and product to ship production-quality training/inference pipelines and model-serving infrastructure.

Design, implement, and test scalable, low-latency ad selection systems using rule-based and ML solutions. Build and maintain microservices, observability, and metrics; translate product stories into tasks; mentor teammates; participate in on-call rotations and own domain areas.

Lead development and productionization of large-scale ranking and personalization ML systems. Build data pipelines, features, and models (batch and real-time), collaborate cross-functionally, run experiments, and improve infrastructure for search, recommendation, and marketplace optimization.

Lead design and production of next-generation search and personalized recommendation systems using LLMs, dense retrieval, transformers, GNNs, and MLOps. Drive model development, GPU-based deployment, and <100ms low-latency ranking while mentoring applied scientists and MLEs and collaborating cross-functionally to productize search experiences.

Support design, training, evaluation, deployment, and monitoring of ML models for document classification, entity extraction, and outcome prediction. Help integrate AI tools into legacy workflows, track metrics (precision/recall/accuracy), assist model versioning and logging, and collaborate across teams while gaining hands-on experience with tax-focused AI systems.

Design, train, test, deploy, and maintain ML models for cyber national security missions. Collaborate with software engineers and data scientists to productionize AI/LLM solutions, fine-tune open-source models, and build automation (e.g., text summarization). Support containerized deployments and on-prem compute; meet security clearance eligibility requirements.

Implement and productionize ML research into performant C++ and Python systems, build ML infrastructure and training pipelines, design LLM/agent applications, and deploy AI workloads across edge, near-edge, and cloud environments while collaborating with data science and engineering teams.

The Manager of Machine Learning Engineering will lead a team to develop and deploy scalable ML solutions, oversee project direction, and ensure responsible AI practices while fostering cross-functional collaboration at Disney.

Lead the machine learning team to develop scalable AI solutions, mentor engineers, and oversee complex ML projects within the Ad Platforms organization.

As a Machine Learning Engineer, you will design core platforms for autonomous agents, engineer data systems, build tools, and enhance product experience while collaborating with data scientists to deploy advanced models.