Top Machine Learning Engineer Jobs

The role involves designing and deploying AI/ML models, collaborating with stakeholders, and enhancing operational efficiency through innovative solutions.

The Senior Software Engineer will design architecture, implement systems for Enterprise AI, collaborate on machine learning development, and support CI/CD practices.

Design and maintain ML infrastructure for swarm UGV systems, implementing robust ML pipelines, data management systems, and CI/CD for model deployment.

Design and develop machine learning infrastructure, enhancing core components for model training, monitoring, and serving. Collaborate with ML engineers on robust pipelines.

The AI/ML Full Stack Engineer will design and maintain applications for predictive insights, develop data pipelines, implement ML models, and collaborate cross-functionally.

As a founding ML research engineer, you'll design AI-powered engineering systems, implement ML algorithms, and integrate deep learning techniques while collaborating with experts in the field.

Design and implement machine learning models and algorithms for AI/NLP-driven services while collaborating with teams for optimization and efficiency.

The role involves developing a virtual assistant platform, collaborating with cross-functional teams, leading a team of engineers, and managing the AI/ML platform while ensuring optimal performance and reliability.

The Staff Software Engineer, AI/ML Focus will lead AI product development, ensure system reliability and security, and collaborate with cross-functional teams to innovate tools and products.

Develop GPU and ML systems to enhance real-time gaming experiences and optimize model deployment on PlayStation hardware. Responsibilities include designing inference pipelines and advanced optimization processes.

The role involves developing and optimizing tokenization systems for machine learning, collaborating with research teams, and improving model training efficiency.

Develop applications for machine learning and generative AI technologies, automate data analysis, and collaborate on prototyping solutions for semiconductor testing.

The role involves building and improving systems for training AI models, focusing on algorithms and infrastructure for reinforcement learning. Responsibilities include enhancing performance, reliability, and usability of systems to support research.

The role involves designing and maintaining machine learning models, analyzing data, and optimizing algorithms to solve business problems.

Design and implement machine learning models to solve business problems, analyze data for insights, and optimize model performance.

Design, build, and deploy ML infrastructure components for autonomous vehicles, focusing on model training pipelines and optimizations.

The role involves optimizing machine learning models, developing infrastructure for model life cycles, and collaborating across teams to enhance Nuro's autonomy technology.

Develop and optimize 3D graphics algorithms for sensor simulation, leveraging GenAI/ML techniques. Collaborate with various teams to enhance simulation realism and generate high fidelity data.

Develop and optimize 3D rendering algorithms for sensor simulation using cutting-edge graphics and GenAI/ML techniques, collaborating with various teams to enhance data fidelity.

As a Senior Machine Learning Engineer on the Perception Mapping team, you'll develop ML models for semantic map detection, integrate them into autonomy systems, and lead technical efforts in model validation and best practices.

Design and develop innovative features for Mirage, managing key software ownership, collaborating with teams to build scalable back-end systems and APIs.

As a Senior Research Engineer, you will optimize language and vision models on Cerebras hardware, focusing on high-performance ML inference techniques.

Develop ML-first behavior modules for predicting future motions of road users. Collaborate with perception and planning teams to enhance autonomous systems.

Design and implement distributed cloud GPU training for ML, build end-to-end pipelines, collaborate on data problems, and uphold ML best practices.

Lead software engineer focusing on optimizing ML models for embedded systems, improving performance and efficiency in production environments.