Top Senior Machine Learning Jobs in Boise, ID

Build scalable systems and machine learning models that transform satellite imagery into labeled geospatial data and production computer vision products. Responsibilities include developing data and annotation pipelines, tracking lineage and versioning, deploying inference services, evaluating models, supporting cloud infrastructure, and improving labeling workflows. The role requires strong Python, cloud, containerization, CI/CD, infrastructure-as-code, production support, and deep learning expertise, with preferred experience in GCP, Vertex AI, spatial databases, and satellite imagery.

Develops and integrates semiconductor process flows for advanced memory and packaging technologies, especially wafer-level and reconstructed substrate processing. Leads technical projects, designs experiments, analyzes process and reliability data, resolves integration issues, and collaborates across engineering and manufacturing teams to qualify scalable technologies. Applies data-driven methods and AI tools to improve workflow efficiency, yield, reliability, and technical decision-making.

Drive product and business impact through statistical modeling, experiment design, Bayesian marketing analysis, machine learning, NLP, LLM applications, and classifier implementation. Build growth metrics, monitoring pipelines, evaluation datasets, and AI trigger systems across search and Duck.ai. Analyze privacy-constrained data, deploy production-grade models, optimize SQL queries, and lead complex projects from scoping through production with minimal direction.

Own the strategy, roadmap, requirements, launch, adoption, and success metrics for a technical platform. Partner with engineering, data, product, and business teams; evaluate architecture, APIs, and system tradeoffs; prioritize foundational investments against customer and revenue outcomes; and apply AI to improve platform capabilities. The role requires strong product management experience, technical depth, analytical skills, and production experience with LLM-based or ML-adjacent products.

Lead the In-Vehicle Experience team at Samsara, focusing on transforming in-cab cameras into intelligent companions. Collaborate with engineering, prioritize features, and ensure client interactions highlight trust and connection. Conduct customer research and integrate AI and ML into product features.

Develop wafer, die, and package-level models across semiconductor technology nodes. Collaborate globally to deploy computational, generative AI, agentic AI, multiphysics, and multiscale modeling solutions using sparse and high-dimensional datasets. Interpret predictive modeling insights, identify engineering findings, and communicate results to technical teams and senior leaders.

Build, operate, and maintain scalable platforms supporting analytics and AI workloads. Responsibilities include cloud architecture across IaaS, PaaS, and SaaS; Terraform automation; Kafka and Kubernetes administration; platform monitoring, observability, cost optimization, reliability, and security controls. The role also provides platform support, documents operational practices, evaluates emerging technologies, and collaborates with architects, vendors, customers, and cross-functional teams.

Develop and optimize full-funnel, multi-channel demand generation campaigns that generate pipeline, accelerate deals, and support cross-sell and upsell opportunities. Partner with product marketing, sales development, content, product, field, partner marketing, and analytics teams. Build lead-nurturing programs, align assets to buyer stages, monitor conversion and ROI metrics, and create dashboards to optimize campaign performance.

Develops compliance analytics for AML, sanctions, and consumer compliance programs. Builds data pipelines, dashboards, scorecards, automated reporting, and data quality processes using dbt, GitLab, Python, SQL, Tableau, and Snowflake. Translates regulatory requirements into actionable insights, risk indicators, and metrics while communicating findings to senior management and regulatory stakeholders.

Leads global semiconductor contamination control strategies across manufacturing sites. Develops monitoring and prevention programs for particles, airborne molecular contamination, metals, humidity, and temperature. Partners with engineering and manufacturing teams to resolve yield, reliability, and quality issues; standardizes metrics and procedures; supports wafer transfers, shipment processes, and capacity expansion. Manages cross-functional projects and applies AI, analytics, and automation to improve process monitoring and operational effectiveness.

Analyze semiconductor electrical, parametric, and fab data to identify yield-impacting design, process, test, and device issues. Develop yield-analysis tools and machine-learning models to predict end-of-line performance, detect failure mechanisms, and improve manufacturing processes. Collaborate with R&D and manufacturing teams, lead troubleshooting efforts, standardize analysis procedures, report experimental results, and support product transfers between R&D and manufacturing sites.

Lead go-to-market strategy for Sprout Social’s AI portfolio, including the Trellis AI agent. Develop enterprise launch playbooks, positioning, executive messaging, campaigns, and enablement programs. Analyze market and competitive dynamics, influence product roadmaps, improve launch processes, and partner with Product, Sales, Revenue, Brand, and executive leadership to drive adoption, revenue growth, deal expansion, and category leadership. The role also mentors marketing team members and operates with substantial autonomy.

Lead agile design and development projects for mobile apps and digital products. Coordinate product, design, engineering, marketing, leadership, and client stakeholders; manage project plans, scope, requirements, backlogs, acceptance criteria, deadlines, workflows, and deliverables. Support scoping and client relationship management while ensuring successful end-to-end software launches and alignment with business objectives.

Develop and operate production ML and AI systems powering patient experiences such as matching, ranking, recommendations, onboarding, and personalization. Build reusable ML infrastructure, pipelines, evaluation, observability, and model-serving capabilities. Provide technical leadership across the ML lifecycle, guide architecture, evaluate custom versus foundation-model approaches, and partner with ML and Patient Engineering teams to deliver reliable, scalable AI products.

Leads the AI Engineering team across applied AI features and foundational ML infrastructure. Responsibilities include managing Senior and Staff engineers, hiring and coaching, deploying LLM-powered capabilities, building MLOps and AI observability platforms, balancing product delivery with platform investment, and ensuring privacy, compliance, evaluation rigor, and clinical safety in production systems.

Own the strategic vision and roadmap for Coursera’s AI-driven search experience. Lead conversational search, personalization, semantic and multilingual search, ranking, and discovery improvements. Partner with engineering, design, research, data science, ML, marketing, and partnerships teams to deliver scalable platform capabilities. Use experimentation, market analysis, KPIs, and customer insights to improve conversion, merchandising, learner experience, and revenue.

Set the technical vision for data science, lead complex statistical and machine learning model development from concept through production, and architect scalable modeling platforms and pipelines. Develop credit risk, pricing, and loss forecasting models; establish model governance standards; partner with senior leaders; mentor data scientists; and communicate technical strategy to executives. The role emphasizes production deployment, monitoring, cloud architecture, and high-impact decision-making across the business.

Performs electrical, physical, and statistical failure analysis for next-generation HBM products from development through production. Identifies defect mechanisms, analyzes component and system data, applies Python, statistical methods, JMP, and AI-enabled tools, and drives root-cause corrective actions. Collaborates with product, process, test, quality, and manufacturing engineering teams to resolve fabrication, assembly, and test issues, support product qualification, improve yield and reliability, and accelerate production ramp.

Develop and maintain production machine learning models, build scalable training and serving technologies, optimize real-time and batch ML pipelines, and monitor model performance and drift. The role involves deep neural networks, recommendation systems, automated retraining, ML research, engineering excellence, and communicating technical concepts to diverse audiences. Candidates need 6+ years of industry ML experience, strong coding skills, and experience deploying neural networks at scale.

Product Counsel will advise product, engineering, data science, marketing, compliance, and sales teams throughout the product lifecycle. Responsibilities include counseling on regulatory compliance, privacy, intellectual property, consumer protection, AI and machine learning, data rights, platform architecture, APIs, identity and account security, and marketing. The role translates legal requirements into technical guidance, supports AI governance, monitors evolving laws, and develops scalable legal processes for a fast-paced technology company.

Drive AI/ML initiatives across storage product development, coordinating engineering, firmware, validation, and data science teams from requirements through deployment. Manage schedules, dependencies, blockers, readiness reviews, portfolio progress, and Go/No-Go decisions. Align priorities with business goals, identify AI/ML opportunities, support adoption of AI-assisted tools, and communicate program status and outcomes to executives and cross-functional stakeholders.

Design, build, and support high-throughput, low-latency LLM inference and data pipelines; collaborate with data scientists to post-train and evaluate models; implement scalable, production-ready systems with strong testing, monitoring, and performance focus; mentor engineers and iterate on architecture, reliability, and user-facing AI applications.

Leads complex enterprise technology initiatives, including ERP implementations, BI, data analytics, AI, and systems integration projects. Manages planning, scope, timelines, resources, budgets, risks, dependencies, stakeholder communications, executive reporting, and cross-functional delivery across Product, Engineering, Data, and business teams. Drives process improvements and ensures projects meet business objectives, customer outcomes, and delivery commitments.

Own the technical sales cycle for Databricks consulting engagements, including discovery, architecture design, solution scoping, estimation, proposals, demos, proofs of concept, workshops, and statements of work. Design lakehouse, data engineering, analytics, governance, security, and AI architectures. Partner with sales, Databricks, alliance, and delivery teams while ensuring effective handoffs into implementation.

Own complex post-sale technical relationships from onboarding through adoption and renewal. Lead implementations, integrations, configurations, and security reviews; advise strategic customers on identity, access, device trust, and SaaS security. Partner with Customer Success and cross-functional teams to drive customer value, manage risks, support expansion, create scalable resources, and mentor team members while serving as an escalation point.