Top Machine Learning Engineer Jobs

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 ML Engineer will develop and enhance machine learning models for credit decisioning, drive technical innovation, and collaborate across teams to deliver impactful solutions for small businesses.

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

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

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

Lead the design and deployment of AI systems for government applications, focusing on geospatial reasoning and agentic infrastructure. Collaborate with various teams to build reliable machine learning systems.

Develop deep learning models for autonomous vehicle trajectories using imitation and reinforcement learning. Collaborate with cross-functional teams to enhance vehicle behavior.

Develop deep learned models and collaborate on generating driving plans for autonomous vehicles, employing imitation and reinforcement learning techniques.

Seeking an AI/ML Engineer to build, train, and evaluate advanced language models, collaborating on technical solutions for government contracts.

The Senior Data Scientist will support U.S. government contracts by developing machine learning algorithms, conducting advanced data analysis, and leading a team of data scientists. Strong collaboration with software engineers and subject matter experts is essential to develop production analytics for mission-critical applications.

Lead collaboration to improve ML workloads on cloud and self-driving cars, optimize models, and mentor junior engineers.

As a Machine Learning Engineer, you'll design and ship LLM-based solutions, collaborate across teams, and improve storytelling capabilities at scale.

Manage and optimize computational infrastructure for training ML models, ensuring scalability for large datasets and performance optimization.

You will build a search and assistant product, improve personalization, train models, and write robust code while mentoring junior engineers.

The Senior Data Scientist will lead AI solutions development, integrating LLMs for business applications, mentoring teams, and shaping data strategy.

The Lead AI/ML Engineer will oversee foundation model projects, focusing on model serving, inference optimization, and deployment, while managing a collaborative team and creating infrastructure for scalable model solutions.

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 Senior AI Ops Engineer will design and deploy scalable AI systems for healthcare automation, focusing on code quality, AI safety, and collaboration across teams.

The Senior AI/ML Engineer will lead AI/ML initiatives, collaborate across teams, and implement data-driven solutions while shaping the company's AI/ML strategy.

Lead design and implementation of LLM observability features: prototype and scale product capabilities for tracing, evaluating, and debugging generative AI systems. Work cross-functionally to influence architecture, mentor engineers, prioritize customer pain points, and drive product and engineering decisions for reliable, high-performance AI observability.

The Lead AI/ML Engineer will architect and deploy RAG pipelines, design Graph RAG solutions, and build scalable ML services, optimizing retrieval quality and implementing observability practices. Collaboration with cross-functional teams and producing clear documentation are key responsibilities.

The Senior Machine Learning Engineer will design, develop, and deploy ML models focusing on computer vision for vehicle damage detection, working with diverse data and cross-functional teams.

Design, build, deploy, and maintain production ML models and data pipelines at scale. Collaborate with product and data science teams, apply ML best practices, monitor model performance, and use cloud and CI/CD tooling to ensure resilient, governed, and explainable ML systems.

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

Design and improve end-to-end ML/AI pipelines, process datasets, and contribute production code for high-quality solutions in machine learning projects.