Top Remote Machine Learning Jobs in Boise, ID

Design, build, and deploy machine learning models for a payments platform, overseeing end-to-end solutions and experimentation, while monitoring models in production.

The job involves building responsive web dashboards, advanced data visualizations, and analytics tools for unmanned aircraft systems, requiring a deep understanding of UI/UX design and data presentation.

As a Machine Learning Engineer, you will fine-tune transformer models, deploy machine learning solutions, and work with cross-functional teams on innovative projects.

Integrate LLMs, RAG, and agentic-AI into secure decision-support software. Design prompts and inference workflows, connect AI to backend services, evaluate outputs, build tests, document designs, and collaborate with engineering, security, and DevSecOps teams to deliver reliable mission-capable AI features.

Lead a team of ML engineers to design, build, and productionize LLM/GenAI and agentic systems (retrieval, tool use, orchestration). Collaborate cross-functionally with MLOps, product, and data science, review code, mentor engineers, run technical evaluations and proofs-of-concept, and ensure reliable, safe ML deployments for a large SaaS product.

The Staff ML/AI Platform Engineer will design, prototype and deploy AI/ML models, optimize pipelines, mentor junior engineers, and collaborate closely with other teams to develop machine learning solutions.

Responsible for fine-tuning state-of-the-art LLMs, optimizing models for deployment, and driving AI architectural decisions. Collaborate with cross-functional teams to develop impactful AI products.

The Machine Learning Engineer will develop and maintain scalable ML systems, collaborate across teams, and innovate solutions for real-time applications to enhance customer experiences.

Embed with strategic higher-education partners to design, build, and ship production AI and platform integrations (LLM/RAG, LTI) across Cengage products. Own end-to-end deployments, write production code, ensure compliance and accessibility, evaluate AI safety and outcomes, mentor FDEs, and translate field learnings into product roadmap contributions.

Own computer-vision accuracy end-to-end: measure per-customer metrics, investigate misclassifications, curate and label datasets, prioritize retraining, and build continuous learning pipelines. Partner with ML/CV engineers and stakeholders to define acceptance criteria, translate findings into actionable fixes, and eventually implement tooling and retraining to ship regular model improvements.

Lead pre-sales and technical strategy for enterprise AI/ML engagements on Microsoft Data & AI. Drive solution design, architecture, executive presentations, and mentorship to close large deals and scale delivery playbooks aligned to security and governance.

Drive delivery excellence and customer success by leading the architecture and implementation of AI/ML solutions while ensuring account growth and client engagement.

Develop and optimize computer-vision and deep-learning models (segmentation, depth) and end-to-end ML pipelines for perception. Own lifecycle from prototype to production deployment on edge and cloud, evaluate performance on large datasets, and collaborate with cross-functional teams to integrate perception into autonomous tractors.

Design and productionize ML models and agentic systems for intraday financial alpha generation. Build data pipelines, experiment frameworks, and portfolio optimization methods. Collaborate with quants and engineers to move models from research to live trading environments.

Lead design, development, and delivery of ML, DL, and Generative AI systems for production. Apply prescriptive analytics, optimization, recommendation, and reinforcement learning. Build and evaluate models, run experiments, develop prototypes, benchmark methods, and collaborate with business partners to translate data into actionable recommendations and influence strategy.

Design, build, deploy, and operate production-grade AI/ML and LLM systems (including RAG and agentic pipelines). Integrate AI capabilities with product workflows, run evaluation and monitoring, optimize model and system performance, and apply strong engineering practices (testing, CI/CD, versioning).

Build and scale production-ready generative AI applications: design multi-agent workflows, implement advanced RAG with vector DBs, integrate/swap LLMs, fine-tune models, and deploy scalable FastAPI/Docker services with monitoring and optimization for latency, cost, and reliability.

Design, develop, and deploy machine learning and deep learning models; build end-to-end ML pipelines including preprocessing, training, evaluation, and deployment; perform feature engineering on large datasets; deploy and monitor models on cloud platforms; collaborate with cross-functional teams to integrate AI solutions and retrain models as needed.

Operate and monitor ML/AI models and agentic systems in production. Build AI observability, logging, tracing, and evaluation pipelines. Monitor LLM outputs, detect drift and model degradation, and maintain data/feature pipelines. Develop CI/CD, model versioning, experiment tracking, and automate alerts and incident remediation while collaborating with data scientists and platform teams.

Design, build, and deploy production-grade generative AI applications: orchestrate multi-agent systems, implement advanced RAG with vector DBs, fine-tune and host LLMs, optimize GenAI workflows, and deliver scalable FastAPI-based backend services with Docker and monitoring.

Design, build, and deploy production generative AI applications: orchestrate multi-agent workflows, implement advanced RAG with vector databases, integrate/benchmark LLMs, fine-tune models, and deploy scalable FastAPI-based services with monitoring, CI/CD, and Dockerized production pipelines.

Design, build, and scale production Generative AI systems: multi-agent orchestration, advanced RAG with vector databases, LLM integration and fine-tuning, scalable FastAPI backends, Dockerized deployment, monitoring, and GenAI optimization for latency, cost, and reliability.

Design, build, and deploy production-grade generative AI systems: multi-agent orchestration, advanced RAG with vector databases, integrate/swap LLMs, fine-tune models, optimize prompts and latency, and containerize services with monitoring and CI/CD.

Design, develop, fine-tune, and deploy generative AI models using deep learning and transformer architectures. Collaborate cross-functionally, troubleshoot model issues, optimize performance, document work, and communicate technical concepts to non-technical stakeholders.

Design, implement, and own end-to-end scalable ML systems and MLOps lifecycle (deployment, monitoring, metrics). Optimize models and system performance, work with Java/Python and TF/PyTorch, use CI/CD, Terraform, Docker/Kubernetes, lead inter-team communication, document work, and meet deadlines.