Top Machine Learning Jobs in Boise, ID

Lead end-user facing engineering squads (about half the team), grow engineering capacity, and provide hands-on technical leadership. Drive distributed computing, cloud (AWS/Azure), CI/CD, Agile/Scrumban, mentoring, conflict resolution, and product delivery for a remote-first, time-zone-compatible global team. Travel periodically for face-to-face team interactions.

Design, build, and deploy novel sensor hardware and data acquisition systems; curate geoscientific datasets; develop statistical, physics-based, and ML models (2D/3D) to predict mineralization; create visualizations and evaluation tools; collaborate with geoscientists, engineers, and vendors; deploy sensors in the field and integrate results to guide exploration programs.

Lead machine learning initiatives, oversee product development, improve execution pace, enhance team reliability, and foster team growth at Hightouch.

The Director of Product Management will oversee the product lifecycle for AI products, focusing on customer collaboration, rapid prototyping, and product strategy. The role involves leading product teams, driving execution, and shaping the product culture.

Lead perception vision, strategy, and roadmap for AV perception. Drive advances in detection, classification, tracking, fusion, and prediction; design sensor suites; define KPIs; hire and manage engineering teams; coordinate cross-functional deployment and issue resolution to ensure robust commercial autonomy performance.

The Enrollment Advisor helps prospective patients through the onboarding process, providing information and support throughout their enrollment in Revero's program.

The VP of Marketing will lead the marketing ecosystem to drive growth, define competitive advantages, influence product design, and mentor a diverse team towards a mission-driven culture.

Lead Billtrust's Applied Intelligence product strategy, build and scale AI product teams, define governance and evaluation standards, translate frontier AI into production-grade AR solutions, and represent the company externally to drive commercial impact and customer co-creation.

As a Senior Staff Machine Learning Engineer, you will drive AI product development, collaborate with cross-functional teams, and enhance ML models at scale.

The SAP Innovation Architect will design SAP automations, mentor engineers, and drive innovation in AI-driven SAP operations, while ensuring scalability and security in integrations.

Act as the technical lead in enterprise sales: run technical discovery, design and deliver customized demos and POCs, assess API/cloud/model fit, address technical objections, support contract negotiations, and relay product feedback to engineering while staying current on AI/ML trends.

Lead design, build, and operation of data architecture, pipelines, and semantic layers. Manage sprint planning, DevOps metrics, data quality and monitoring, and MLOps enablement. Partner with stakeholders, coach engineers, track project plans, and ensure data availability, accuracy, and documentation.

Manage and grow strategic partnerships across US & Canada for a Google Cloud-focused AI and data consultancy. Drive co-sell initiatives, GTM planning, partner enablement, joint marketing, event execution, partner onboarding, and pipeline generation while tracking partner performance and reporting impact.

Lead strategy and build 0->1 AI products that streamline complex healthcare workflows across providers, pharmacies, patients, and pharma partners. Own roadmap, requirements, metrics, and product performance; partner with engineering, data, clinical, and commercial teams to launch and iterate AI-enabled solutions that drive adoption and measurable business and patient outcomes.

Lead enterprise business systems (NetSuite, Salesforce, Workday, Boomi) strategy and delivery. Architect integrations and AI/ML workflows, manage delivery teams and vendors, improve SDLC, ensure data alignment with analytics, and drive automation to increase operational efficiency and measurable ROI.

Develop scalable machine learning services for data enrichment, managing the ML lifecycle from model training to deployment, and ensuring performance and reliability standards.

Define and execute the companys AI strategy; build and lead the data science, ML engineering, and AI teams; govern intake, prioritization, and AI guardrails; advance production ML, MLOps, LLMs and agentic workflows; partner cross-functionally to deliver measurable ROI; and report AI impact and roadmap to the CEO and board.

Lead backend architecture and implementation for scalable, reliable cloud-hosted web products. Mentor engineers, set standards, integrate agentic AI coding tools, design systems applying ML to large datasets, and balance customer-facing features with technical debt and performance.

Lead the Contracts product roadmap for a B2B healthcare SaaS platform, applying deep reimbursement and managed care expertise. Partner with AI engineers to build contract-understanding models, drive adoption, iterate on workflows, create business cases, and coordinate cross-functionally with Legal, Finance, Sales, and SMEs to ensure compliant, revenue-aligned contract execution.

Lead engineering team to define and execute threat detection, platform, analytics, and automation roadmaps. Advance automated, AI-augmented detection and response across SIEM/EDR/NDR/SOAR and cloud environments, partner with Incident Response, and drive measurable risk reduction via metrics and team development.

Lead technical engagements with public sector customers, onboard and train users, analyze infrastructure and workflows, drive migration from on-prem to cloud HPC, collaborate with architects and product teams, and present Rescale solutions to grow customer adoption and improve the ScaleX platform.

Design and execute research for early, noninvasive cancer detection using cfDNA. Lead molecular and clinical data analysis, develop high-throughput NGS bioinformatics pipelines, collaborate with wet lab teams to assess assay performance and iterate on experimental methods, and propose computational improvements for assays and models.

Embed with customers to discover needs, design and implement production-grade integrations (APIs, data pipelines, event-driven workflows), deploy and troubleshoot solutions, train IT teams, and drive adoption and measurable business outcomes.

Develops and deploys machine learning systems for high-stakes decisions in pricing, claims, underwriting, and predictive health. Responsibilities span problem framing, quantitative research, experimentation, causal inference, predictive modeling, multimodal and foundation model development, agent systems, production deployment, and ongoing iteration. The role requires strong mathematical and statistical foundations, Python fluency, and expertise in machine learning, statistics, econometrics, or causal inference.

Build and deploy computer vision and machine learning solutions for power grid infrastructure, including defect detection, thermal anomalies, vegetation monitoring, and intrusion surveillance. Responsibilities span research implementation, model experimentation, error analysis, data pipelines, experiment tracking, model serving, testing, monitoring, and client delivery. The role requires productionizing models, adapting research papers, optimizing architectures, and independently owning projects from problem definition through deployment.