Top Remote Machine Learning Jobs in Boise, ID

Lead GoDaddy's three-year Generative AI and ML roadmap, architecting and scaling agentic and personalized AI solutions globally. Own end-to-end ML systems, enable rapid experimentation, ensure governance/compliance, and partner across product, engineering, and analytics. Mentor and grow high-performing teams and drive adoption of advanced AI techniques to improve customer experiences and business outcomes.

Design, train, and deploy ML models for Qur'ān recitation, tajwīd detection, and ASR. Build data pipelines for cleaning, segmentation, labeling, and evaluation. Integrate real-time predictions via APIs or lightweight on-device inference, optimize models for speed and mobile performance, and collaborate with backend developers to ship AI features in the app.

As an AI/ML Data Contributor, participate in data labeling, annotation, content evaluation, and user testing for machine learning projects.

Develop and prototype learning-based models using data-centric techniques; design and maintain multi-modal data and feature extraction pipelines; build auto-labeling systems; perform ETL and optimize SQL for analysis/ingestion; collaborate with data infrastructure and ML engineers to integrate workflows; write maintainable code and participate in code reviews to support scalable ML production.

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.

As a Senior ML Engineer, you will lead the design and operation of GenAI solutions, focusing on LLM development and deployment, mentoring colleagues, and collaborating with cross-functional teams to enhance AI capabilities for the insurance sector.

Design and implement infrastructure to support generative AI and machine learning, optimizing workflows and integrating new technologies while ensuring reliability and performance.

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 developing, adapting, and implementing machine learning algorithms for computer vision tasks in power grid analysis. Responsibilities include experimentation, error analysis, and project ownership.

The Sales Executive will develop new AI and ML service opportunities, manage sales pipelines, and engage with technical decision-makers primarily in the US and Canada. Responsibilities include proposal shaping, pipeline management, and client relationship management with a focus on complex technology sales.

Serve as an AI/ML-focused solution engineer for Healthcare & Life Sciences: provide technical guidance, design cloud data and lakehouse architectures on Snowflake, lead demos and proofs-of-concept, support sales cycles, collaborate with product and engineering, and communicate AI/ML value to technical and executive stakeholders.

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.

Develop and productionize NLP and ML solutions (including LLMs and GenAI) to extract insights from unstructured data. Manage research, data preparation, model training/fine-tuning, evaluation, and cross-functional integration into production systems, focusing on sparse data, model explainability, and performance.

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.

As a Senior ML Engineer for Payments, you will develop AI solutions for payment experiences, optimize transaction processing, and ensure system reliability while mentoring teammates and collaborating with cross-functional teams.

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

Build and deploy low-latency, production-grade ML models and end-to-end pipelines for fraud detection. Engineer high-frequency features from massive event data, maintain automated MLOps for continuous training/deployment, optimize runtime scoring across distributed systems, and collaborate with infrastructure, product, and data science teams to translate fraud patterns into scalable algorithmic solutions.

As a Staff Data Scientist, you will lead initiatives to build and optimize machine learning models, collaborate across departments, and solve complex business problems using data.

The Principal Machine Learning Engineer will define technical direction, architect scalable systems, lead cross-organizational initiatives, mentor engineers, and communicate effectively at all levels.

Build and design backend services, AI agents, and machine learning models. Collaborate cross-functionally to deliver AI features and maintain code quality.

The Senior ML Data Scientist will develop machine learning models to detect fraud, manage the data science lifecycle, and collaborate with teams on strategies.

Lead design, training, fine-tuning, and deployment of ML and generative models (LLMs, diffusion). Build scalable MLOps pipelines, vectorization and data ingestion, integrate models into microservices for low-latency inference, implement evaluation/alignment/bias-mitigation, optimize models (quantization, distillation), and mentor engineers while influencing the AI product roadmap.

As a Senior Machine Learning Engineer at ZoomInfo, you will enhance core datasets through ML/NLP solutions, manage entity resolution tasks, and drive cross-functional impact by mentoring teams and collaborating on ML KPIs.

Develop and iterate deep learning models for protein-ligand co-folding and structure prediction, benchmark performance, integrate validated models into production drug-discovery pipelines, analyze structural and sequence data, communicate findings, and collaborate with multidisciplinary teams to scale research into products.