Top Remote Machine Learning Engineer Jobs in Austin, TX

Design and deploy ML solutions for clinical data, collaborating with cross-functional teams to translate healthcare challenges into scalable systems. Maintain reusable codebases and provide guidance to junior engineers.

As a Software Engineer II, you'll develop backend systems, support ML platform features, collaborate across teams, and ensure system availability.

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

The Senior Machine Learning Engineer will develop ML APIs, build data pipelines, and collaborate across teams to enhance ML models and applications.

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

The Research Engineer will collaborate with scientists to develop and validate machine learning models for speech technologies, manage scalable training systems, and design tools for accessibility to non-technical users.

The role involves leading the technical development of a production ML application, mentoring junior engineers, and collaborating on architecture and strategy. The candidate will take ownership of the project and grow within the company as it evolves.

Lead architecture and engineering for production-grade ML in healthcare: design MLOps CI/CD, scalable low-latency inference for LLMs and classical models, build reliable data pipelines for FHIR/HL7/DICOM, manage cloud IaC and observability, ensure HIPAA/HITRUST compliance, and mentor engineering teams to operationalize agentic AI workflows.

The ML Engineer will contribute to the automated discovery engine, developing RL frameworks, synthetic data engines, and evaluating performance using proprietary datasets.

Design, develop, and deploy machine learning models; analyze large datasets; build scalable data pipelines and distributed computing environments; integrate ML solutions with cross-functional teams; optimize performance and support production ML infrastructure.

Lead the development of AI systems for Trustbridge and manage junior engineers, focusing on production deployment and technical strategy.

The Senior Staff Machine Learning Engineer will develop scalable ML infrastructure, define best practices, and enable rapid deployment of AI/ML models at Airbnb, collaborating with cross-functional teams.

Analyze malware and detection data to improve machine learning models, manage false positives, and respond to internal inquiries regarding threat detections.

The Machine Learning Engineer will design and build systems that improve the AI shopping agent by using real user feedback, focusing on feedback loops and metrics to enhance user interactions and agent performance.

Work with the product team on automating data ETL pipelines, deploying ML models, and connecting ML models to user interfaces. Collaborate with cross-functional teams on product development.

Design and deliver customer experience solutions using machine learning techniques, analyze customer data, implement ML algorithms, and automate processes.

As a Machine Learning Engineer, you'll design, build, and maintain models to detect security threats, ensuring model efficiency and aligning initiatives with business goals.

The Junior-Midlevel Developer will build and enhance Dumroo.ai's AI-powered education platform, collaborating on software components, data pipelines, and machine learning features.

The Forward Deployed ML Engineer will integrate AI/ML models into clinical software, assist in model fine-tuning, and engage with clients to ensure success in healthcare deployments.

The AI/Machine Learning Engineer will develop systems for automating health benefits decisions, integrating large language models with rule-driven patterns, and ensuring compliance with regulations.

The Machine Learning Engineer will develop software solutions, optimize data pipelines, and deploy machine learning models using cloud platforms and best practices.

Lead the advancement of embedding-based retrieval systems, focusing on multimodal models, model performance, and extensibility for various use cases.

You will build and own end-to-end ML systems, focusing on training, evaluation, deployment, and integration with applications, while ensuring quality and efficiency.

Senior AI/ML Engineer responsible for designing, building, and deploying AI/ML systems, mentoring teams, and leading cross-functional collaborations to drive AI solutions.

As a Machine Learning Engineer at Shipwell, you'll build and scale AI-powered logistics solutions, maintaining data pipelines and ML infrastructure across the organization.