Top Machine Learning Engineer Jobs in Austin, TX

The role involves designing and developing a distributed ML data platform for autonomous vehicles, collaborating with stakeholders, and mentoring engineers while ensuring operational excellence.

The Staff Engineer will design and manage ML infrastructure while mentoring teams, driving MLOps strategies, and ensuring compliance in healthcare data systems.

Develop and deploy machine learning solutions for autonomous truck applications: EDA, deep learning model development, embedded deployment, data ingestion/curation, analytics, visualization, technical leadership, and process improvement across manufacturing and business domains.

Lead the development of ML models and prototypes for Edge AI/ML, integrate with mixed-signal systems, and optimize deployment. Collaborate with cross-functional teams and mentor engineers while exploring new ML trends and applications.

Design and develop machine learning systems for construction robots, including scalable infrastructure, computer vision models, and data analysis for performance improvement.

This role involves designing, deploying, and maintaining machine learning and AI models in production, managing ML pipelines, and ensuring system reliability and performance.

The Machine Learning Engineer will develop editable visuals combining computer vision and machine learning, enabling chat-driven figure editing and leveraging biological understanding to produce accurate visuals.

Design, implement, and optimize real-time computer vision and machine learning models for an autonomous anti-drone turret. Integrate algorithms with embedded hardware, collaborate with electrical engineers, and test/validate performance across environments while hardening prototypes into military-grade systems.

Lead development and optimization of real-time computer vision and ML models for an autonomous gun turret, integrate algorithms with embedded hardware and sensors, validate performance across scenarios, mentor junior engineers, and help harden prototypes for military-grade deployment.

Lead end-to-end design, development, and productionization of large-scale ML systems (recommendation, ranking, personalization). Architect scalable pipelines (data processing, training, deployment), build and optimize models (including LLMs), establish experimentation best practices, collaborate cross-functionally, and mentor engineers to drive responsible, impactful AI features.

Lead technical strategy and build production-grade recommendation, ranking, and personalization ML systems. Design, deploy, and scale models and ML pipelines (Spark, Airflow), develop/fine-tune LLMs and AI Agents, run experimentation (A/B tests), mentor senior ICs, partner cross-functionally, and embed responsible AI practices.

Lead research, design, and deployment of large-scale foundation models for machine-generated data (logs, time series, traces). Define AI strategy, mentor senior engineers, partner with product and exec teams, and drive production-grade model training, optimization, and MLOps for enterprise-scale applications.

Design, build, and deploy production-grade AI/ML and LLM-based systems. Lead end-to-end ML lifecycle, implement RAG and embedding pipelines, establish MLOps best practices, mentor engineers, and collaborate with cross-functional teams to deliver scalable, responsible AI solutions.

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.

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.

Design, build, and deploy scalable ML models and pipelines to enhance a talent marketplace and GenAI solutions. Lead experimentation (A/B tests, statistical validation), improve production ML performance, ensure code and data quality, work with cross-functional teams, mentor junior engineers, and propose innovative AI-driven solutions aligned to business goals.

The Staff AI/ML Engineer will lead the development of AI systems for regulatory compliance, focusing on ML architecture, implementation, and oversight of model reliability.

The role involves conducting research and optimizing machine learning systems while collaborating with technical staff to enhance product performance.

As a Research/ML Engineer, you will translate cutting-edge AI research into product solutions, develop safety and reliability systems, and collaborate with technical teams.

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.

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

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.

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