Top Machine Learning Engineer Jobs

Lead hands-on technical leadership in machine learning engineering, mentor peers, oversee infrastructure delivery, and foster innovation in AI/ML solutions at Xometry.

Develop AI-powered solutions for personalized content and marketing. Collaborate with teams to optimize ML models and pipelines at scale, mentoring engineers and driving strategic growth initiatives.

As an AI & ML Engineer, you'll develop, deploy, and maintain ML models, collaborating with teams to solve real-world problems using ML algorithms in an Agile environment.

As a Senior/Staff Machine Learning Engineer, you'll architect and build ML systems, develop models for shift pricing and fill rates, and work on computer vision tasks, mentoring junior engineers along the way.

Develop and optimize computer vision models for drones, improve ML pipelines, collaborate on R&D, and tackle advanced technical challenges.

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.

As an ML Engineer Intern, you will build and deploy ML models, design scalable infrastructure, and collaborate with data science teams in a fast-paced environment.

As an ML Engineer, you'll build and deploy scalable ML systems, optimize models, and collaborate with teams to improve content across platforms.

Design and maintain multilingual datasets, develop data pipelines, implement quality filters, and analyze dataset biases while collaborating with researchers.

Optimize inference latency and throughput for large-scale ML models, collaborating on performance tuning, and building inference-serving systems.

The ML Engineer will optimize large-scale model training pipelines, improve distributed training strategies, build robust infrastructure, and collaborate on training techniques and performance metrics.

The Machine Learning Engineer will develop and innovate ML models and their infrastructure, collaborate, mentor, and take ownership of impactful projects.

Design and implement knowledge distillation pipelines, optimize training and inference performance, and collaborate with research on production-ready ML models.

The Applied Machine Learning Engineer will develop machine learning models, transform data science prototypes, and build efficient self-learning applications for greenhouse operations.

As an AI Engineer, you will design and build AI systems for enhancing search, personalization, and fraud detection, collaborating with data scientists and engineers to deliver production-ready AI solutions.

As a Machine Learning Engineer, you'll develop AI systems, focusing on autonomous agents, ML pipelines, and production-grade solutions while collaborating with cross-functional teams.

The role involves building ML systems, improving models across product surfaces, and leveraging data for personalization and user engagement.

Design and deploy machine learning models for creating HD maps, utilizing advanced ML techniques and collaborating with other teams to deliver reliable solutions.

This role involves advancing autonomous vehicle simulations using Generative AI technologies, managing the lifecycle of product innovations, and collaborating in an ML engineering team.

Seeking a Senior AI/ML Engineer with expertise in machine learning frameworks to support a U.S. Government customer on critical software systems.

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.

As a Machine Learning Engineer, you'll create and optimize production-ready ML solutions, manage the ML lifecycle, and conduct experiments.

Lead the development of BEV models for multi-modal perception, driving architectural innovation and large-scale model training in autonomous systems.

Design and implement machine learning algorithms for robotic manipulation, focusing on RL and IL. Collaborate across disciplines to ensure integration and successful deployment.

Responsible for designing and implementing reinforcement learning algorithms, conducting experiments, and optimizing models for robotic applications, collaborating with multiple teams.