Top Senior Machine Learning Jobs in Boise, ID

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.

Drive enterprise AI transformation across the Marketing organization by managing the AI roadmap, governance council, intake processes, use-case prioritization, training, change management, responsible AI practices, and adoption KPIs. The role coordinates cross-functional stakeholders, translates technical capabilities into business plans, develops enablement resources, and reports progress and ROI to senior leadership. This is an execution and organizational adoption role rather than a technical AI development position.

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.

Leads roadmap governance, executive reporting, cross-functional coordination, risk management, dependency tracking, and performance measurement for Micron’s AI Security organization. The role partners with security teams including Security Operations, GRC, Third-Party Risk, Security Awareness, and Security Architecture to align initiatives with leadership priorities, improve operating rhythms, and drive execution across the enterprise.

Lead AI-driven smart manufacturing solutions across semiconductor operations, improving yield, quality, test coverage, and performance. Partner with engineering, quality, data science, machine learning, architecture, and data engineering teams to identify opportunities, define requirements, prioritize solutions, and deliver end-to-end projects. Manage Agile product development, establish success metrics, quantify business value and ROI, and drive digital transformation through production deployment.

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.

Perform FIB lamella preparation and TEM analysis (microstructure and chemical) using electron diffraction, EDS, EELS, and advanced STEM techniques. Optimize lab procedures, support process development and product transfers, and develop AI/automation and data-processing workflows for TEM data acquisition and analysis.

Design, build, deploy, and support scalable Java microservices on AWS. Provide technical leadership, architectural direction, code reviews, troubleshooting, proof-of-concept development, and cross-functional collaboration. Work with high-throughput data, messaging systems, distributed architectures, observability, and AI-assisted development tools. Participate in on-call support, mentor engineers, lead architectural initiatives, and deliver reliable SaaS platform features.

The Senior Data Scientist supports Customer Success through deep-dive analysis, predictive modeling, causal inference, experimentation, and KPI development. The role analyzes churn, retention, renewals, upsell opportunities, and revenue trends; diagnoses metric shifts; builds data pipelines and dashboards; and develops AI/LLM-powered solutions. The scientist collaborates with stakeholders, communicates insights, improves data quality, and measures the impact of customer success initiatives and product changes.

Own the strategic vision and roadmap for Coursera’s search experience, including conversational, personalized, semantic, multilingual, and AI-driven discovery. Partner with engineering, design, research, data science, marketing, and product teams to improve search accuracy, conversion, and learner engagement. Lead experimentation, KPI measurement, competitive analysis, and revenue-focused personalization initiatives across a global learning platform.

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.

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.

Lead GitLab’s company-wide strategy for autonomous, agentic software development workflows. Identify and validate AI opportunities across the SDLC, build prototypes and reference architectures, establish evaluation frameworks and safety controls, and drive internal adoption. Partner across Engineering, Product, Infrastructure, Security, Architecture, and Data and ML teams to scale reliable, observable, compliant agentic capabilities into customer-facing products. Mentor senior technical leaders and represent GitLab externally on AI-assisted development and productivity measurement.

Analyze semiconductor process flows, inline defect data, and metrology signals to identify anomalies, conduct root-cause investigations, and improve defect performance. Support experimentation, baseline monitoring, defect-reduction initiatives, process optimization, and technology transfers in high-volume manufacturing. Collaborate with global manufacturing, process integration, process development, yield engineering, and data science teams to deploy best-known methods and improve yield and quality.

Develops EDA/CAD software, automation, and AI-assisted tools for custom memory IC design workflows, including layout, verification, simulation, and tape-out. Collaborates with global design and CAD teams to identify automation opportunities, integrates and qualifies EDA tools, and applies generative AI, machine learning, and LLM technologies to improve productivity, quality, and cycle time. Maintains scalable, tested, documented technical software and workflow integrations.

Leads technical customer engagements for strategic enterprise accounts, combining solution engineering, software implementation, consulting, and sales support. Responsibilities include discovering requirements, designing and documenting SailPoint solutions, conducting foundational implementations and proof-of-value engagements, educating customers on architecture and best practices, and developing AI-enabled demonstrations and agentic solutions. The role collaborates across sales, product, professional services, partners, and customer success, and may require up to 15% travel.

Build and operate production machine learning systems for Safety AI, including low-latency APIs, data pipelines, model serving, evaluation, monitoring, and rollout infrastructure. Process large-scale camera and telematics data, optimize cloud and edge-to-cloud execution, track model performance and drift, and partner with applied scientists, firmware engineers, platform teams, and product managers to deliver reliable safety features.

Engineer AI-powered threat detections, automated response playbooks, and continuous adversary emulation. Apply machine learning and LLMs to security operations, conduct threat hunting and AI red teaming, map coverage to MITRE ATT&CK, and develop detection-as-code through CI/CD. Build production-grade Python integrations, measurable containment capabilities, and safe autonomous defenses with guardrails. Mentor security professionals and collaborate across threat response, cyber defense engineering, and platform teams.

Design, build, and operate high-throughput, low-latency ad-serving infrastructure processing millions of requests per second. Optimize GPU-powered TensorRT inference, feature stores, model serving, benchmarking, experimentation tooling, and system reliability. Partner with machine learning engineers to productionize models and own systems through design, deployment, observability, capacity planning, incident response, and optimization.

Leads Toast’s AI and analytics strategy for Enterprise Care. Owns the roadmap, evaluates and deploys AI-assisted support tools, develops operational intelligence products, and partners with Product, Engineering, and Data teams to move pilots into production. Establishes complexity and workload intelligence, assesses AI readiness, translates AI performance into measurable operational outcomes, and builds organizational AI literacy. The role requires extensive enterprise support or service-delivery leadership, hands-on production AI experience, operational data fluency, and the ability to drive trusted adoption among agents and executives.

Build and productionize traditional machine learning models, owning feature engineering, validation, deployment, monitoring, drift detection, and retraining. Develop LLM-powered agentic tools, dashboards, self-serve analytics products, and automated insight pipelines. Partner with engineering and business stakeholders, write production-grade Python and SQL, drive adoption, improve data quality, and establish responsible AI workflows for a regulated industry.

Own and optimize process loop performance for semiconductor manufacturing modules. Lead root cause investigations, characterize failure mechanisms, drive yield and structural improvements, support NPI transfers and technology ramps, and deploy monitoring, BKMs, and AI/advanced analytics to accelerate problem solving and manufacturing readiness across global sites.

Own and improve Headway’s privacy program across HIPAA, 42 CFR Part 2, state privacy laws, individual rights, incidents, vendor governance, and AI use. Maintain controls, data maps, metrics, training, and operational workflows. Conduct product, vendor, and AI privacy assessments; manage rights requests and incident investigations; coordinate notifications and regulatory submissions; and translate complex requirements into practical guidance for cross-functional teams.

Develop autonomy evaluation metrics, statistical and machine-learning analyses, dashboards, and pipelines for simulation and on-road autonomous vehicle testing. Analyze perception, prediction, and planning performance; identify anomalous behavior and critical scenarios; apply VLMs and LLMs where appropriate; and support release gating and safety decisions. The role requires production Python, C++ debugging, technical leadership, cross-functional collaboration, and rigorous software engineering practices.