Top Machine Learning Jobs

Design, develop, and deploy production-grade machine learning models and pipelines. Preprocess and analyze large datasets, collaborate with cross-functional teams, monitor model performance, and implement scalable ML solutions to improve customer experiences.

Lead initiatives to enhance the ML platform, mentor junior engineers, build scalable ML models, and ensure code quality while driving innovation in a startup environment.

The Machine Learning Engineer will develop production-grade ML pipelines, optimize research code, and implement cloud infrastructure for ML workloads at Harmonic.

Lead the scientific direction and machine learning development for climate modeling. Manage model training, evaluation, and collaboration across teams to develop predictive systems based on hydrology and climate data.

The Senior Machine Learning Engineer will design and deploy ML models, mentor junior engineers, and contribute to project architecture, enhancing security applications.

The AI/Machine Learning Engineer II will design and develop models to analyze data for health monitoring devices, focusing on diagnosis and prediction using various algorithms.

Develop innovative machine learning algorithms for modeling proteins, collaborate with biologists, and refine predictions through experiments.

Design, implement, and maintain RAG pipelines and LLM integrations, build embedding generation and retrieval systems, develop REST API integrations, and refine Postgres schemas to support tactical intelligence data workflows in onsite, mission-focused environments.

The Senior AI/ML Engineer will develop AI systems for enterprise decision-making, focusing on LLMs and real-time data processing. Responsibilities include building detection systems, insight engines, evaluation frameworks, and collaborating with engineers to ensure robust AI product engineering.

Design and scale machine learning systems and infrastructure, partner with data and applied scientists, build reusable ML pipelines and abstractions, ensure production readiness using robust software engineering practices, and prototype new technologies to improve customer experience and automate business processes.

Design, build, test, deploy, and maintain production AI capabilities (agents, workflows, models, MCP servers, APIs) across a SaaS dental platform. Train and operate ML models, implement RAG and vector DB solutions, use AI-assisted tools, collaborate in design and code reviews, and support production reliability and monitoring.

The ML Engineer will optimize large-scale model training pipelines, improve distributed training strategies, build robust infrastructure, and collaborate on training techniques and performance metrics.

Lead training, adaptation, evaluation, and productionization of Lila's AI models for scientific customer workflows. Build evaluation loops, design experiments, debug model failures, integrate model improvements into product workflows, and create reusable tooling for model adaptation, evaluation, and deployment while collaborating across research and software teams.

The Machine Learning Engineer will develop AI capabilities, improve data processing methods, and focus on machine learning algorithms for data analysis and target detection.

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

As a Staff Machine Learning Engineer, you will develop models and agents for AI integration into products, focusing on performance monitoring and issue resolution.

Design, build, and validate ML models for passive RF emitter identification; perform exploratory data analysis on RF sensor datasets; implement and maintain CPU-only ML pipelines for constrained, air-gapped edge hardware; collaborate with scientists and engineers; produce reproducible experiments, documentation, and status reports.

As the founding Machine Learning Engineer, you'll own end-to-end credit risk modeling, from data pipeline design to production deployment, collaborating with cross-functional teams to shape modeling decisions and define future hires.

The Machine Learning Engineer will develop and maintain ML deployment pipelines, operationalize AI models, and ensure system reliability and scalability.

As a Machine Learning Engineer, you will build end-to-end ML systems for autonomous underwriting, collaborating with underwriters and engineers to advance ML capability.

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.

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

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

The role involves developing and deploying machine learning models for forecasting within the Camus platform, requiring collaboration and technical depth in ML.

The Senior Software Engineer is responsible for designing, developing and maintaining AI-powered services, enhancing ML capabilities, and leading cross-functional teams in a collaborative environment to innovate and implement best practices in document intelligence.