Top Machine Learning Engineer Jobs in Austin, TX

You will own the compilation stack transforming AI models into optimized inference for autonomous driving. This includes developing tools and techniques for model export and optimizing performance while ensuring safety and reliability.

The Senior Compiler Engineer will develop a model compilation toolchain for deploying optimized machine learning models in autonomous vehicles, focusing on performance engineering and collaboration with cross-functional teams.

The role involves designing, implementing, and optimizing CUDA-based kernels for autonomous vehicles, ensuring high-performance ML inference for AV software.

The Senior Software Engineer will lead projects in building AI/ML workflows, developing model services, and enhancing APIs, while collaborating across teams to drive technical excellence and operational success.

The Machine Learning Engineer will maintain and expand the ML platform, working on scalable systems, model serving, and collaborating closely with cross-functional teams.

The Senior Machine Learning Engineer will lead the development of scalable ML systems, mentor team members, and improve existing data infrastructures while collaborating with various stakeholders to execute strategic initiatives.

The Senior Staff Machine Learning Engineer will drive the architecture and delivery of AI systems focused on fraud detection and risk assessment, leading initiatives across teams while mentoring engineers.

The Senior Machine Learning Engineer will develop advanced fraud prediction models, utilizing anomaly detection and supervised learning techniques to enhance fraud prevention efforts and collaborate with cross-functional teams to integrate these models into the organization's systems.

Lead technical projects for the Workers AI team focused on deploying AI inference, building innovative features, and enhancing developer experience.

The Senior Full-Stack Engineer II will lead technical projects, develop AI application services, maintain APIs, and collaborate on system architecture and product design.

Develop scalable machine learning services for data enrichment, managing the ML lifecycle from model training to deployment, and ensuring performance and reliability standards.

Design, train, optimize, and deploy efficient on-device ML models (audio and vision) for resource-constrained products; maintain Ambiq ADKs; port and optimize customer models; apply model-efficiency techniques; produce customer-facing demos, docs, and benchmarks.

The Machine Learning Engineer will develop and optimize models, manage datasets, enhance training pipelines, and collaborate across teams to innovate in autonomous vehicle technology.

Lead machine learning initiatives to enhance underwriting model accuracy. Design and implement advanced training strategies and optimize workflows. Mentor engineers and influence modeling strategy.

The Machine Learning Engineer will design, build, and maintain production machine learning systems, focusing on scalability, reliability, and integration with existing products. Responsibilities include developing ML pipelines, establishing ML Ops best practices, and collaborating on product capabilities.

The On-Device Machine Learning Engineer will optimize and deploy ML models on consumer hardware, enhancing performance while ensuring privacy and reliability. Responsibilities include model optimization, lifecycle management, and collaboration with product teams to achieve high user experience benchmarks.

The Senior Software Engineer will lead projects focused on building an AI platform for managing models, collaborating on features, and maintaining high performance in a scalable environment.

Lead development of scalable ML infrastructure, focusing on compute for simulation, data labeling, and data generation. Collaborate with ML engineers, set technical roadmaps, ensure system reliability, and research advanced computing techniques.

The engineer will build and operate AI/ML infrastructure, managing services on AWS and bare metal, using tools like Kubernetes and Terraform.

Develop ML/RL models and infrastructure for AV simulations, optimize training loops, and support project timelines in autonomous vehicle tech.

Lead design and implementation of enterprise-grade AI systems, architect AI infrastructure, and drive technical strategy for AI-powered products primarily for military applications.

Develop and optimize machine learning models for data extraction from tax documents, collaborate on software integration, and mentor junior team members.

Summer ML engineering intern to assist in designing, training, and evaluating models, build RAG systems and data pipelines, perform EDA and research, collaborate cross-functionally, document results, and contribute to testing and QA.

The ML Engineer will develop motion planning models using ML techniques, simulate interactions for outdoor delivery robots, and collaborate across teams for integration.

As a Machine Learning Engineer, you will design, train, and deploy ML models for motion planning, develop data pipelines, and integrate models for real-time use in autonomous vehicles.