Top Machine Learning Jobs in Boise, ID

The Software Engineer will develop machine learning infrastructure, optimize models, and create tools for data analysis while collaborating with a diverse team.

The Senior Machine Learning Engineer will design and maintain ML solutions for fraud detection, collaborating across teams and establishing MLOps best practices.

As a Senior AI Engineer, you will develop and optimize Generative AI applications, manage AI pipelines, collaborate with clients, and ensure production delivery with MLOps best practices.

As a Senior Machine Learning Engineer, you will drive AI initiatives, collaborate across teams, and develop scalable ML infrastructure to improve healthcare.

The Lead ML Ops Data Scientist will design scalable MLOps systems, mentor data scientists, and develop models for rental matching and personalization.

The role involves sourcing and recruiting senior engineers in AI, ML, and related fields, building talent pipelines, and collaborating with engineering leadership.

As a Machine Learning Engineer, you'll design and implement detection systems, enhance model efficacy, and analyze datasets to improve email detection against security threats.

Lead the design and implementation of deep learning models for biological data, focusing on model architecture, project leadership, and innovation in machine learning applications in biology.

Develop and tune ML models, productionize and deploy strategies for ML systems, and align cross-functionally to communicate performance metrics.

As a Staff Machine Learning Engineer, you will develop, productionize, and deploy ML models for clinical use, ensuring real-world effectiveness through hands-on coding and infrastructure building.

The role involves improving machine learning ranking models, implementing algorithms, and taking full ownership of machine learning systems within Quora's product.

The ML Engineer will leverage deep learning for natural language processing tasks, impacting Entefy's products while requiring strong machine learning fundamentals and NLP expertise.

The Staff Machine Learning Engineer will develop and maintain MLOps tools, optimize model performance, and collaborate on ML system architecture with a focus on enhancing developer experience.

The role involves leading a team to develop and optimize ML models and systems at scale, ensuring reliability and operational excellence while collaborating across various departments. Responsibilities include hiring, coaching, and guiding technical decisions.

The role involves deploying and optimizing AI/ML models, integrating them into products, building APIs, and ensuring reliability through testing and monitoring. Candidates will work in a collaborative environment to enhance their MLOps and software skills.

The Senior ML Engineer will design evaluations for AI features, optimize pipelines, and ensure quality in ML systems while collaborating with cross-functional teams.

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.

The Senior Software Engineer I role involves developing scalable solutions, mentoring junior engineers, and collaborating on technical roadmaps within the healthcare industry.

Lead ML strategy and delivery for Documents & Biometrics. Define AI/ML roadmap, drive technical excellence across model lifecycle, build scalable MLOps and data pipelines, mentor senior teams, ensure ethical, compliant ML deployment, and collaborate cross-functionally to deliver measurable business impact.

Lead design, development, and productionization of advanced ML models and data pipelines. Drive architecture, experimentation, validation, and best practices while mentoring ML engineers and delivering scalable, business-driving 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.

Lead and grow a global data engineering and ML organization, design cloud-native data platforms and pipelines, deliver ML-powered products, establish engineering and MLOps best practices, and support sales through solutioning and client-facing technical leadership.

Lead a team of data scientists developing ML-driven products, managing infrastructure, training models, and ensuring production quality while collaborating with senior leadership.

Design, operate, and optimize GPU-backed ML/LLM inference infrastructure in cloud environments. Build scalable GPU clusters, implement high-throughput and distributed inference, integrate optimization stacks, schedule heterogeneous workloads, and add observability to improve performance and reliability.