Top Machine Learning Engineer Jobs

Lead and drive strategy for generative AI projects, develop cutting-edge models, engineer data pipelines, and collaborate across teams to implement AI solutions.

Lead the Generative AI team by setting technical direction, developing large-scale models, and mentoring team members while collaborating across departments.

Lead the Generative AI team by setting technical direction, developing models, creating data pipelines, and driving AI strategy across Xometry's marketplace.

Lead and architect the technical strategy and roadmap for Content Quality at Pinterest, focusing on building signals for content quality and collaboration with ML engineers on deployment.

The role involves designing and optimizing bidding algorithms for real-time bidding systems, leading projects, and enhancing ad performance through advanced AI techniques.

In this role, you'll design algorithms for real-time bidding systems, enhance forecasting capabilities, and engage in A/B testing while collaborating with cross-functional teams.

The Staff Machine Learning Engineer will lead ML strategy, architect AI infrastructure, mentor engineers, and develop impactful AI systems.

Design and implement AI-enabled workflows to accelerate silicon development across frontend and backend design. Build generative AI, RAG, and agentic systems integrated with EDA tools, write production Python/C++ pipelines, process design databases/netlists, and develop evaluation frameworks and benchmarks for design quality.

Design and build scalable, permission-aware retrieval and reasoning systems for Zoom's AI Companion: vector and hybrid search, RAG pipelines for agentic reasoning, ranking and evaluation frameworks, indexing and NLP/entity extraction pipelines, and collaborate with product, infrastructure, and research to ship production-grade LLM-based features.

Lead design, build, and production deployment of ML and GenAI solutions (LLMs, RAG, multi-agent systems, recommender and RL models). Architect scalable, secure inference and MLOps pipelines, mentor MLEs, collaborate with stakeholders to define roadmaps, and measure model performance and business impact. Implement fine-tuning, retrieval, orchestration, and monitoring for enterprise-grade AI products.

Designs, develops, deploys, and maintains production AI/ML models for intelligence applications. Ensures compliance with DIA QAF, performs model optimization, validation, bias testing, monitoring, and documents model cards for accreditation.

Design and scale infrastructure for machine learning models, build pipelines, and optimize for performance in a collaborative remote environment.

This role involves building and optimizing ML systems and frameworks, collaborating with teams to enhance research and development in ML models.

Lead design, training, and deployment of large-scale ML models (LLMs, diffusion, GNNs). Own end-to-end training pipelines, distributed training, evaluation, and inference architecture. Collaborate with data and infra teams, drive reproducible model development, and provide technical leadership and mentorship from research through production.

Design, build, deploy, and maintain production-scale ML models and infrastructure. Collaborate with cross-functional Agile teams to create scalable data pipelines, leverage cloud and Kubernetes-based architectures, ensure CI/CD, model governance, monitoring, and responsible/explainable AI practices while leading and mentoring ML engineering efforts.

Design, build, deploy, and maintain production ML systems at scale. Collaborate with Product and Data Science to create ML models, data pipelines, and cloud-based architectures, apply CI/CD and monitoring, retrain and govern models, and ensure Responsible and Explainable AI practices.

Lead design, development, deployment, and productionization of scalable ML systems. Build and optimize models, data pipelines, and cloud-based architectures; automate CI/CD, monitor and retrain models, ensure governance, responsible/explainable AI, and collaborate with cross-functional Agile teams.

Build and operate ML training and serving infrastructure by designing, developing, and launching backend systems. Collaborate across teams to decompose projects, support operations and on-call, create monitoring and metrics, perform code reviews, and contribute to developer velocity and platform reliability.

As a Staff Machine Learning Engineer, you will lead the development and deployment of AI models for autonomous driving, focusing on safety and reliability. Responsibilities include driving the roadmap, training models, and collaborating across teams.

As a founding Machine Learning Engineer, you will improve core models, optimize LLM inference, develop data pipelines, and ship features directly to users.

As a Machine Learning Engineer, you'll build and optimize ML systems for music promotion, ensuring they are efficient and scalable while collaborating with cross-functional teams.

As a Senior Principal Machine Learning Engineer, you'll lead technical direction, drive scalable model development, and mentor teams in a dynamic environment, optimizing Disney's ad technology.

Develop and optimize medical large language models, collaborate with interdisciplinary teams, and maintain state-of-the-art machine learning techniques to enhance medical education and clinical decision support.

The AI/ML Engineer designs and operates a production-grade orchestration platform, focusing on multi-agent workflows, LLM integration, and backend systems. Responsibilities include managing cloud-native infrastructure, ensuring system reliability and observability.

Design, implement, and support AI/ML systems, working with business units to optimize workflows and educate staff. Communicate findings to stakeholders and ensure compliance with policies.