Top Machine Learning Engineer Jobs

Design and implement model serving architectures, develop monitoring tools, and optimize performance for generative media models working with Applied ML teams.

Lead the ML Ops team, overseeing model deployment, monitoring, and infrastructure for machine learning systems while mentoring engineers and driving optimization.

The Senior Machine Learning Engineer will design and build systems for processing social media data, implementing infrastructure for ML systems, and working with advanced AI models.

Develop and maintain scalable ML infrastructure, collaborate with teams, and implement production ML systems while ensuring efficiency and reliability.

Design and develop scalable ML systems for healthcare, collaborating with teams to enhance products and maintain efficient infrastructures.

Own, train, and optimize computer-vision models for deployment on edge devices. Ensure models meet specifications and customer needs, deploy models in large codebases, and work with embedded Linux using C++ and Python.

Lead the development of high-performance ML platforms, guide the software lifecycle, and collaborate with teams to drive innovation in Generative AI and networking solutions.

The Senior Machine Learning Engineer will build and own production ML systems, manage end-to-end workflow, debug issues, and mentor others.

Develop and deploy AI/ML applications for containerized production environments: analyze datasets, train and build inference models, prototype software architecture, debug customer issues, and collaborate with global ACS teams and product engineers to support deployments and research.

Intern will develop AI/ML applications for containerized production environments, perform model selection and training, prototype software architectures, research technologies, and support customers by debugging and collaborating with global ACS teams.

The Senior Machine Learning Research Engineer will develop and deploy deep learning pipelines, optimize model training, and collaborate with scientists to enhance AI/ML capabilities for cancer detection.

The role involves building ML infrastructure, creating data pipelines, ensuring data quality, and collaborating with data scientists to optimize models.

Senior ML Engineer on the Vision team responsible for framing ML problems, improving data acquisition and training pipelines, designing experiments, addressing model drift, building monitoring/visualization tools, collaborating across teams, and reviewing ML engineers' code to deploy robust models.

Own productionalization of ML prototypes: deploy, monitor, and maintain scalable, reliable ML services for campaign optimization. Improve model quality, latency, observability, testing loops, data freshness, and rollout safety while sharing on-call responsibility.

The Senior Software Engineer II at Aledade will develop scalable AI/ML solutions, mentor junior engineers, and collaborate with cross-functional teams in the healthcare industry.

Design, build, and maintain scalable ML infrastructure and CI/CD pipelines; containerize and deploy ML models; automate provisioning with IaC; monitor production model performance, availability, and security; manage ML team databases and enforce version control, testing, and documentation best practices.

Seeking a Senior Machine Learning Engineer to design and implement ML systems, optimize deployment, and collaborate with data teams, focusing on scalability and performance.

Manage the ML lifecycle from data collection to deployment, collaborate across teams, and optimize code for production ML services.

The Staff ML Platform Engineer will enhance Afresh's ML platform performance and scalability, collaborating with ML teams to improve systems and deploy models efficiently.

The Senior Engineer in Machine Learning will develop algorithms using machine learning techniques on biomedical datasets, prototype solutions, and collaborate with teams to ensure data quality and performance measures.

Build and improve AI/ML systems and data pipelines for a B2B SaaS platform; shape product and technical strategy; work with customers; mentor team members; ensure secure, robust, and scalable infrastructure.

Define and enforce ML quality strategy and acceptance criteria across data, model, and system levels. Design evaluation suites, metrics, and test datasets; automate ML QA using Python and CI/CD; build test harnesses and quality gates for training and deployment; evaluate CAD RL outputs; mentor teams, participate in code reviews, and document findings.

The Staff Machine Learning R&D Engineer will innovate machine learning algorithms for spatial computing, collaborate with teams, and translate research into production solutions.

Design, develop, and implement machine learning models and algorithms. Collaborate with teams, optimize models, and ensure data quality for production deployment.

The job involves developing and optimizing machine learning models, overseeing model risk, and collaborating with teams on AI-related projects.