Top Machine Learning Engineer Jobs

The role involves developing AI and ML solutions for national defense, focusing on efficiency and decision-making through advanced software engineering practices.

The Senior Machine Learning Engineer will develop AI inference services, optimize workflows, and mentor team members while collaborating on impactful projects.

As a Staff Software Engineer in ML Platform at Stripe, you will lead complex projects, influence technical strategies, and mentor teams to enhance machine learning systems that power Stripe's products.

Lead the design and optimization of batch and streaming data pipelines, ensuring high-quality data delivery for AI and analytics, while mentoring and promoting engineering best practices.

The role involves researching and implementing machine learning techniques for various applications, collaborating with scientists, and adapting methods for high-performance computing. Responsibilities include developing models, leading research, and presenting results in a collaborative environment.

The Senior Machine Learning Engineer will design and build distributed systems for personalized content recommendations, ensuring deployment and optimization of ML platforms.

This role involves building ML infrastructure for monetization, including developing data pipelines, model training platforms, and real-time serving systems, while ensuring performance and reliability.

Join Hiring.Cafe as a Founding Machine Learning/AI Search Engineer to develop AI recommender systems and scalable infrastructure, contributing to a transformative job search engine.

The Machine Learning Engineer will develop production-grade ML pipelines, optimize research code, and implement cloud infrastructure for ML workloads at Harmonic.

The Cloud and ML Infrastructure Engineer will maintain complex systems, manage AWS production infrastructure for ML/AI workloads, and design monitoring systems for algorithms.

The Lead Machine Learning Engineer will design and operate scalable ML systems, lead teams, guide technical direction, and ensure alignment with business goals across advertising applications.

Develop innovative machine learning algorithms for modeling proteins, collaborate with biologists, and refine predictions through experiments.

Design, build, and operate production-grade ML systems for personalization, search, and recommendations. Own end-to-end pipelines from data ingestion and modeling to deployment, monitoring, and iteration, ensuring scalable, reliable, and observable services.

Design and maintain datasets, build and evaluate domain-specific ML and vision-language models for real-time privileged access threat detection; deploy and optimize Python/Docker inference services integrated with WebSocket/WebRTC and protocol-level interfaces; monitor and document production models.

Design and implement AI solutions to automate workflows, enhance decision-making, and ensure security in sensitive environments, requiring deep technical expertise.

The Senior Software Engineer is responsible for designing, developing and maintaining AI-powered services, enhancing ML capabilities, and leading cross-functional teams in a collaborative environment to innovate and implement best practices in document intelligence.

The AI ML Operations Engineer will design and manage machine learning systems from data collection to deployment, ensuring system reliability and optimizing workflows.

As an Autonomy Engineer, you will design and implement scalable data pipelines, manage ML workflows, and improve AI training infrastructure for drones.

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.

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.

The role involves designing and implementing production-grade machine learning systems for conversational AI experiences, focusing on user engagement and context management.

Design, develop, and maintain large-scale distributed systems and optimize the ML model delivery pipeline. Collaborate with research and backend teams to improve model training, serving, performance, and scalability.

Lead end-to-end silicon diagnostic pipeline using AI/ML to analyze scan failures and process data, build models for failure classification and predictive yield, orchestrate AI-based EDA tools, and drive cross-functional root-cause analysis to improve yield and time-to-market.

Lead the Snowflake Machine Learning Platform development, ensuring operational excellence and collaborating on innovative ML solutions to enhance customer capabilities.