Top Machine Learning Jobs in Boise, ID

Improve semiconductor manufacturing process quality, yield, equipment performance, and efficiency. Monitor process and equipment issues, analyze manufacturing data, apply SPC, FDC, DOE, and structured problem-solving methods, and partner with operations and engineering teams. Lead process optimization, productivity, cost reduction, documentation, training, and corrective-action initiatives supporting high-volume memory production.

Supports semiconductor design rule development, documentation, validation, and optimization for advanced DRAM technologies. Develops scripts, automation solutions, methodologies, and workflows using EDA tools and AI-supported analytics. Performs design rule checking, physical verification, simulation, data analysis, and layout debugging while collaborating with design, CAD, process integration, verification, and product engineering teams.

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.

Start up, develop, optimize, and troubleshoot semiconductor manufacturing equipment, including pumps, abatement systems, and chillers. Drive yield, quality, productivity, cost, and risk improvements through failure analysis, SPC/FDC, FMEA, and 8D methodologies. Coordinate equipment evaluations, qualifications, supplier audits, and process changes while monitoring equipment health, resolving manufacturing issues, and developing redundancy plans. Analyze performance data and apply automation, coding, machine learning, and JMP scripting to improve semiconductor production.

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.

Own and scale Jellyfish’s security engineering program, including strategy, architecture, secure SDLC governance, AI security, threat modeling, vulnerability remediation, incident response, penetration testing, and bug bounty programs. The role combines hands-on software development with executive advising, cross-functional leadership, mentorship, and security risk management across SaaS infrastructure and customer-facing AI products.

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 AMP's multidisciplinary engineering organization to define and execute technology vision and roadmap. Responsible for scaling teams across hardware, software, AI, and infrastructure; driving product development, quality systems, and reliable deployments; partnering with execs and cross-functional teams on budgets, prioritization, and metrics; recruiting and mentoring leaders; and balancing short-term delivery with long-term investments to create business value.

Build scalable data and machine learning pipelines for cloud-based model training and deployment. Develop, fine-tune, and productionize traditional, deep learning, generative AI, multimodal, and agentic systems. Implement feature engineering, event-driven ETL/ELT, model monitoring, CI/CD, RAG, prompt engineering, and REST APIs. Optimize model performance and compute costs while collaborating with Product and Engineering teams on enterprise Generative AI adoption and roadmap development, including applications for semiconductor manufacturing data.

Develops automation, data pipelines, databases, analytics, machine learning models, reporting tools, and web applications for package technology quality assurance. Analyzes manufacturing, product, and reliability data to support risk assessment and root-cause investigations. Collaborates with engineering teams, communicates findings, trains users, maintains software and reports, and shares analytics best practices across global teams.

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.

The GRC Analyst manages third-party risk assessments, vendor due diligence, security policies, risk analysis, internal audits, and SOC 2 audit support. The role involves maintaining the GRC program, tracking findings, coordinating stakeholders, promoting security awareness, and using AI and machine learning to automate compliance processes. Candidates should have 1–2+ years of relevant experience, strong communication skills, and familiarity with risk methodologies, vendor assessments, policies, and compliance audits.

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.

Supports semiconductor manufacturing equipment by monitoring performance, performing preventative maintenance, troubleshooting mechanical and electronic systems, assisting installations, and improving uptime. The intern collaborates with engineering and technician teams, investigates root causes, applies SPC, DOE, and 8D methods, implements corrective actions, and maintains procedures and training documentation. The role is hands-on in a fabrication facility and emphasizes safety, equipment reliability, process control, and continuous improvement.

Supports semiconductor probe equipment installation, qualification, upgrades, troubleshooting, maintenance, and reliability improvement. Analyzes manufacturing and equipment data, develops scripts and automation solutions, and applies AI, machine learning, predictive maintenance, SPC, and DOE to improve equipment availability, quality, and factory efficiency. Collaborates with manufacturing, facilities, quality, process integration, suppliers, and other engineering teams while maintaining documentation and safety compliance.

Develop and support software, automation tools, dashboards, and alarms for cloud infrastructure, cost attribution, and cloud efficiency. Collaborate with cross-functional teams and senior engineers to design reliable systems, APIs, databases, tests, and AI agents. Contribute to architecture and automation strategies while maintaining strong security, communication, and delivery practices.

Leads the strategy, technical direction, and delivery of insurance data science across pricing, underwriting, cost modeling, demand modeling, and growth. Oversees predictive modeling standards, model lifecycle governance, engineering collaboration, regulatory alignment, and production deployment. Builds and develops a high-performing data science organization while partnering with actuarial, product, underwriting, pricing, and executive leadership to connect analytical work with profitability and business outcomes.

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.

Leads Micron’s global DRAM packaging organization, defining long-term technology strategy and delivering advanced packaging solutions including HBM, 3D integration, heterogeneous integration, and chiplets. The role oversees cross-functional product development through high-volume manufacturing, drives technology investments, develops customer and supplier partnerships, and leads digital transformation using AI, analytics, automation, and engineering productivity initiatives. It also requires building high-performing global teams and influencing business and technology direction.

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.

Leads technical direction for GitLab’s Core DevOps services, covering CI, CD, planning, and source control. Defines architecture, quality metrics, migration strategies, and technical roadmaps; addresses systemic risk and complex design decisions; prototypes and writes code when needed; partners with Infrastructure, Security, SRE, and Product; adopts AI capabilities; mentors senior engineers; supports incident response and senior technical hiring.

Analyze large datasets, identify meaningful patterns, develop machine learning models, and deliver actionable insights for business decisions. Collaborate with cross-functional teams to understand data needs, communicate findings clearly to stakeholders, and stay current with advances in data science and AI technologies.

Owns operations for Optain’s nationwide network of contracted optometrists and ophthalmologists. Responsibilities include creating provider policies and SOPs, running quality and performance programs, modeling capacity, managing recruitment, handling complaints and urgent findings workflows, and preparing governance decisions for the Medical Director. The role partners with Product and Engineering to automate network processes and requires experience in healthcare operations, clinician networks, clinical quality, credentialing, and multistate licensing.