Top Machine Learning Jobs in Boise, ID

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 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.

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.

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.

Contribute to next-generation DRAM technology development by defining design rules, structural requirements, and electrical specifications. Collaborate with process, device, materials, simulation, architecture, circuit design, and quality teams. Maintain integration plans and technical documentation, assess device architectures and scaling approaches, resolve technical issues, and use AI-assisted tools and data-driven methods to support engineering analysis and decision-making.

Supports the safe, reliable operation and continuous improvement of bulk and specialty gas systems used in semiconductor manufacturing. Analyzes performance data, troubleshoots equipment, leads root-cause investigations, applies AI and predictive analytics, develops dashboards and digital solutions, ensures safety and quality compliance, and supports upgrades, installations, commissioning, documentation, maintenance strategies, and cross-functional reliability projects.

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.

Semiconductor yield analysis internship focused on reviewing experimental data, identifying device performance shifts and process anomalies, improving measurement accuracy, and using inline data and machine learning to predict cell performance. The intern will collaborate with process integration, process engineering, and data science teams while developing root-cause analysis, process improvement, and technical communication skills.

Internship focused on developing semiconductor metrology processes and measurement technologies. Responsibilities include designing and running cleanroom experiments, analyzing semiconductor metrology data with Excel and JMP, evaluating new tools, optimizing measurements with process engineers, and supporting next-generation technology development and fab transfers. The role also applies data science, AI, and machine learning to metrology analysis and workflow improvement.

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.

Supports thin films semiconductor equipment engineering projects involving equipment and process data analysis, optimization, structured experiments, technical documentation, and cross-functional collaboration. The intern investigates equipment hardware, process interactions, and operating conditions while applying data analytics, artificial intelligence, and machine learning tools to improve engineering workflows and equipment performance.

Conduct spectroscopy and microscopy experiments on semiconductor thin films, surfaces, and interfaces. Process, analyze, and visualize photoluminescence, fluorescence lifetime, Raman, and AFM-IR data; correlate signatures with material properties, process conditions, and defects. Develop validated characterization workflows, maintain laboratory documentation and safety standards, collaborate with scientists and engineers, and present findings in a technical report and final presentation.

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.

Designs and implements scalable AI, machine learning, automation, and generative AI solutions for clients. Responsibilities include assessing AI readiness, developing strategies, leading discovery and solution delivery, integrating AI with enterprise systems, advising on governance and responsible AI, managing client engagements, contributing to reusable AI offerings, and supporting business development. The role requires strong technical, consulting, communication, and stakeholder management skills.

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.

Provide system administration for server platforms and virtualization: monitor performance, manage incidents/changes, perform capacity planning, patching, and lifecycle activities. Coordinate with teams/vendors, participate in on-call and emergency response, and develop automation and AI-assisted tooling to improve operational efficiency and reduce incidents.

Own and maintain mask-shop semiconductor equipment, troubleshoot and improve uptime, lead installations and projects, apply data analytics/automation/AI for predictive maintenance, and create documentation and training to support manufacturing and equipment reliability.

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.

Own the strategy and roadmap for patient-provider search, retrieval, ranking, and marketplace matching. Lead experimentation, objective functions, evaluation, and ML platform foundations while partnering with ML engineers, data scientists, design, clinical teams, and executives. Improve successful, lasting care matches rather than optimizing only clicks or bookings. Balance patient access, provider growth, clinical quality, and payer priorities, while setting the standard for technically grounded product management.

Analyze malware, binary files, customer detection tickets, and machine learning detections to improve detection efficacy and manage false positives. Review product detections, assess legitimacy and risk, address internal detection questions, and provide insights to data science teams. The role collaborates with technical account managers, incident response teams, data scientists, and malware researchers.

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.

Define architecture and metrics for autonomous driving evaluation, lead cross-functional integration across autonomy and simulation teams, develop statistical and machine learning methods, analyze large-scale time-series data, manage evaluation KPIs and release gates, and communicate safety and readiness insights through dashboards and stakeholder presentations.

Serves as the technical bridge between customers, Sales, and technology teams. Leads technical discovery, solution architecture, demonstrations, workshops, proofs of concept, and evaluations using HERE data, APIs, mapping, routing, spatial analytics, cloud, AI, and enterprise integration. Partners with Sales to qualify opportunities, reduce technical risk, develop proposals and reusable presales assets, and guide customers toward location-intelligence solutions.

Analyze malware, binary files, customer detection tickets, and machine learning detections to improve detection efficacy and reduce false positives. Review product detections, assess binary legitimacy, address internal detection questions, and provide insights to data science teams. The role collaborates with technical account managers, incident response teams, data scientists, and malware researchers. Required capabilities include malware analysis, reverse engineering, Python scripting, binary fundamentals, threat assessment, research methods, operating systems, and AI-assisted workflow improvement.