Top Machine Learning Jobs in Boise, ID

Analyze semiconductor inspection data, process flows, and manufacturing metrics to identify defects, excursions, and yield-impacting issues. Support root cause analysis, experimentation, process control, defect reduction, and process optimization in high-volume manufacturing. Use statistical analysis, automation, AI-enabled tools, and data visualization to improve process visibility and manufacturing efficiency. Collaborate with cross-functional and global engineering teams to transfer best practices and maintain process stability.

Develops and implements automated FIB-TEM workflows for semiconductor process development, materials analysis, product transfer, and yield improvement. Performs FIB lamella preparation and analytical TEM using STEM, EELS, 4D-STEM, EDS, diffraction, and automation scripts. Applies AI, machine learning, Python, and data science to improve data acquisition and analysis. Collaborates with research and manufacturing teams, optimizes laboratory procedures, and supports shift handoffs.

Perform electrical, physical, and statistical failure analysis on HBM semiconductor products. Identify defect mechanisms and root causes using engineering methods, statistical analysis, AI-enabled tools, and data analytics. Collaborate with product, process, test, quality, and manufacturing teams to resolve fabrication, assembly, and test issues. Support product qualification, production ramp, yield improvement, reliability enhancement, and corrective-action implementation while communicating technical findings across multidisciplinary teams.

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.

Develops and deploys physics-based, statistical, and machine learning models for semiconductor manufacturing. Builds AI-enabled VPM and SPOCK workflows using machine learning, generative AI, LLMs, and agentic AI. Responsibilities include model lifecycle management, automation, validation, monitoring, process optimization, yield improvement, and collaboration with global engineering teams. The role applies predictive and prescriptive modeling to DRAM, HBM, and technology-transfer applications.

Leads a team of semiconductor equipment engineers responsible for improving manufacturing capability, equipment performance, yield, throughput, quality, and cost. Sets team strategy, develops talent, allocates resources, and collaborates with manufacturing, facilities, technology development, suppliers, and global engineering partners. Drives equipment optimization, installation, modification, and relocation initiatives while communicating technical and business updates to leadership and cross-functional audiences.

Supports semiconductor yield improvement through wafer testing, electrical failure analysis, manufacturing data analysis, root-cause investigations, and automation. Develops scripts, dashboards, visualizations, statistical analyses, and AI-assisted workflows using generative AI, prompt engineering, and automation techniques. Collaborates with design, process integration, manufacturing, test, and product engineering teams, presenting technical findings and contributing to process optimization and engineering productivity.

Lead EUV lithography process development and sustainment for advanced semiconductor nodes. Drive process-window optimization, scanner and metrology strategy, root-cause analysis of CDU, overlay, stochastic, and defectivity issues, and high-yield manufacturing improvements. Collaborate with equipment, materials, etch, integration, suppliers, and research partners while using data-driven and AI-assisted methods to improve process performance, yield, and engineering productivity.

Develop next-generation HBM and Near Memory DRAM products, including 2.5D and 3D solutions. Collaborate with customers, research, manufacturing, testing, architecture, and quality teams to define requirements, resolve silicon issues, ensure test coverage, improve build readiness, and deliver products on schedule. The role requires advanced electrical or computer engineering expertise, semiconductor knowledge, analytical problem-solving, technical leadership, and strong cross-functional communication.

Lead silicon photonics and heterogeneous integration development from concept through manufacturing readiness. Responsibilities include process-flow integration, photonic device and advanced packaging development, DOE execution, process characterization, yield improvement, defect reduction, root-cause analysis, design-rule development, tapeouts, technology transfers, technical roadmaps, and AI-enabled engineering initiatives. The role collaborates with design, device, packaging, manufacturing, and supplier teams to deliver optical connectivity technologies for high-performance computing and AI infrastructure.

Lead Micron’s AI-driven security transformation across physical security, SOC/GSOC operations, threat intelligence, and insider risk. Own the converged AI and data architecture, scale AI agents and automation into production, establish risk-based security assessment, and deliver measurable improvements in detection, response, workload reduction, and risk visibility. The role provides technical leadership, mentors engineers and data scientists, and partners with IT, cybersecurity, legal, privacy, and senior leadership.

Perform electrical, physical, and statistical failure analysis for new HBM products. Analyze manufacturing and characterization data, identify root causes, and collaborate with Product, Process, Quality, and Test Engineering teams to resolve yield and reliability issues. Use Python, AI-enabled tools, and statistical or machine learning methods to detect trends, improve component performance, communicate corrective actions, and support product qualification and production ramp.

Investigate complex AML cases, review transaction-monitoring alerts, determine SAR/STR filing decisions, and communicate findings to regulators, law enforcement, and stakeholders. The role also supports audits, improves AML detection models and typology coverage, analyzes transactional data, identifies financial crime trends, and provides analyst training. Candidates need strong blockchain, AML regulations, investigative writing, and blockchain forensic tool experience, with AI and SQL capabilities preferred or advantageous.

Develops capacity models, utilization strategies, and capital planning analyses across a global semiconductor manufacturing network. Evaluates process, equipment, technology, and supply chain changes; leads capacity optimization studies; and partners with cross-functional teams to improve throughput, equipment utilization, capital deployment, and manufacturing flexibility. Applies advanced analytics, AI, modeling, automation, and decision-support tools to resolve complex planning challenges and communicate actionable recommendations to technical and business stakeholders.

Provides global technical leadership for dry etch processes across semiconductor manufacturing. Defines strategy, improves yield, defectivity, process capability, equipment productivity, cycle time, and cost efficiency, and scales process and equipment solutions across DRAM and NAND sites. Leads technical programs, supplier roadmap influence, governance, knowledge sharing, analytics, automation, and AI initiatives while mentoring senior engineers and technical leaders.

Develop software, automation, dashboards, and web-based tools that help engineers analyze semiconductor manufacturing and product data. Responsibilities include processing large datasets, visualization, workflow automation, AI-assisted engineering applications, debugging, testing, documentation, and collaboration with engineering teams. The intern will own a project from problem definition through implementation and demonstration while gaining experience at the intersection of software, data analytics, and semiconductor manufacturing.

Provides global technical leadership for thin-film deposition across semiconductor manufacturing. Defines process strategy, improves yield, defectivity, equipment productivity, cost, cycle time, and capability, and leads qualification and scale-up of deposition technologies. Partners with engineering, manufacturing, facilities, automation, and suppliers to influence equipment roadmaps and deliver high-volume manufacturing solutions. Establishes governance and knowledge-sharing practices, mentors technical leaders, and advances analytics, automation, AI, and continuous improvement.

Leads AI and automation product strategy for disability and absence claims operations. Responsibilities include prioritizing AI opportunities, owning roadmaps, designing human-in-the-loop workflows, defining safeguards and success metrics, partnering with engineering, coaching Product Owners, managing delivery increments, and communicating operational impact to senior leadership.

Lead B2B activation marketing to drive patient starts from commercial referral channels by building scalable systems, attribution frameworks, and repeatable playbooks. Implement MarTech and data-driven strategies, run A/B and incrementality tests, automate campaign execution using AI/ML and LLM tools, and templatize activation campaigns while managing and growing a cross-functional team.

Owns strategic planning, governance, prioritization, risk management, and execution across a portfolio of Superintelligence Lab programs. The role establishes phase-gate standards, manages change, tracks portfolio health through dashboards and metrics, resolves blockers, advises cross-functional teams, and communicates progress to executives. It requires strong technical program management experience, Jira proficiency, Agile and SDLC knowledge, and the ability to apply AI technologies to improve business outcomes.

Build and deploy production-grade AI and machine learning solutions for marketing and sales, including LLM agents, data-quality pipelines, semantic search, RAG, analytics, and integrations with Salesforce, Marketo, and Adobe platforms. Define evaluation and governance practices, monitor AI services, implement privacy controls, and collaborate with platform teams. Train and mentor technical and business stakeholders on responsible AI adoption while supporting DevOps, incident response, and operational runbooks.

Intern will analyze semiconductor defect and metrology data, apply AI and machine learning to classification and reporting, support SPC and DOE initiatives, investigate measurement deviations, monitor tool health, troubleshoot fab data systems, and improve automation and process control. The intern will complete a project, document results, and present findings at the end of the three-month internship.

Evaluates AI use cases for technical feasibility, data readiness, implementation complexity, risks, dependencies, and operational value. Coordinates cybersecurity, privacy, legal, architecture, data governance, and delivery reviews; facilitates alignment among technical and business stakeholders; manages AI portfolio intake and reporting; identifies reusable capabilities; communicates recommendations and status; and improves intake frameworks, assessment methods, governance playbooks, and operational dashboards.

Develop and validate new DRAM process nodes and products from silicon bring-up through qualification, yield improvement, and production ramp. Analyze electrical, probe, simulation, and characterization data; optimize memory, logic, and analog circuits; conduct experiments; investigate process and manufacturing issues; and recommend data-driven improvements. Collaborate cross-functionally while using Python, statistical tools, automation, and approved AI-assisted engineering tools for analysis and problem-solving.

Own and improve advanced semiconductor manufacturing equipment performance, reliability, uptime, and capability. Responsibilities include troubleshooting, repairs, preventive maintenance, root-cause analysis, equipment upgrades, installations, vendor coordination, manufacturing support, and cross-functional project execution. The role also develops equipment analytics, dashboards, automation, predictive maintenance, and AI-assisted troubleshooting solutions using engineering and data tools. Additional duties include technical documentation, procedure development, and technician and engineer training.