Top Remote Machine Learning Jobs in Boise, ID

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.

Leads executive discovery, consulting opportunity strategy, solution architecture, proposals, SOWs, commercialization, and delivery mobilization for enterprise data, analytics, AI, and GenAI engagements. Advises clients on modern data platforms, cloud modernization, governance, security, ML, and responsible AI. Requires strong presales, delivery leadership, workshop facilitation, architecture, estimation, and commercial skills across Snowflake, Databricks, Microsoft Fabric, AWS, Azure, and Google Cloud.

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.

Develops data science models, analytical processes, visualizations, dashboards, and reports for U.S. Navy electronic warfare systems. Integrates logistics, engineering, maintenance, reliability, and operational data to evaluate system readiness, reliability, maintainability, performance, risks, and lifecycle sustainment. Applies data mining, statistical analysis, NLP, and machine learning to support operational decisions and collaborates with engineers, logisticians, analysts, and program personnel.

Executes systems engineering across R&D platforms, hybrid cloud, SaaS, HPC, containers, integrations, data pipelines, observability, and AI architectures. Coordinates Enterprise IT, vendors, cybersecurity, compliance, and scientific stakeholders to deliver secure, scalable, validated, and supportable solutions. Develops technical documentation, deployment plans, runbooks, monitoring, lifecycle artifacts, risk logs, and operational handoffs. Implements RAG, vector database, AI gateway, agentic workflow, and multimodal integration patterns while supporting cloud migration, change management, governance, and technical issue resolution.

Build and operate Lakehouse data pipelines, product features, machine learning and generative AI components, testing, monitoring, instrumentation, and documentation. Collaborate with product managers, engineers, and business users to clarify requirements, deliver production solutions, investigate defects, and improve reliability and performance. Participate in code reviews, technical design discussions, and backlog-driven development while following engineering standards and contributing to scalable Data & AI products.

Leads application engineering teams delivering cloud-native, AI-enabled solutions on Microsoft Azure for Medicare and Medicaid claims-processing operations. Oversees architecture, software development, quality automation, integrations, support, governance, security, vendor relationships, and large-scale implementations. Develops Azure applications as needed, mentors staff, manages cross-functional initiatives, ensures HIPAA compliance, and partners with business and executive stakeholders to improve healthcare payer outcomes.

Drives 100% new-business sales for Conga’s SaaS solutions across assigned commercial accounts. Responsibilities include prospecting, territory and account planning, solution selling, managing opportunities through the sales cycle, closing enterprise-wide deals, exceeding revenue quotas, and building strategic partner relationships. Collaborates with sales engineering, marketing, customer success, professional services, and other internal teams to understand client needs and deliver tailored solutions.

Lead biostatistical support for Phase I–IV clinical trials, including study design, sample size calculations, exploratory and efficacy analyses, simulation studies, statistical analysis plans, regulatory submissions, and interpretation of results. Provide strategic guidance to cross-functional teams, oversee vendor-supported work, apply advanced statistical methods, and communicate technical findings clearly to technical and non-technical stakeholders.

Build and deploy machine learning models on large healthcare datasets, extract actionable insights, develop scalable data processing systems, collaborate with cross-functional teams, and communicate findings to stakeholders to improve patient outcomes.

Develop, test, and integrate AI software for aerospace and industrial applications. Interns will use Python, JavaScript, and C++ to build AI solutions, evaluate data and algorithms, collaborate with machine learning and data engineers, monitor AI trends and regulations, and present progress to leadership and stakeholders.

Design and lead the technical strategy for search across Vannevar’s products. Build and improve full-text, vector, and LLM-powered retrieval systems, optimizing latency, relevance, index quality, freshness, and performance. Collaborate with ML, product, design, and mission strategy teams to translate user needs into search innovations. Apply expertise in information retrieval, NLP, machine learning, indexing, ranking, query parsing, tokenization, vector search, and evaluation frameworks.

Lead research on improving AI agent training data and evaluations. Manage and mentor research engineers, define research direction, design rigorous experiments, validate data quality, diagnose reward and grading failures, and translate findings into scalable tools, workflows, and standards. Collaborate with engineers, domain experts, and vendors while communicating technical conclusions and tradeoffs clearly.

Build and maintain mission-critical wealth management systems for SMB clients. Design data pipelines, ETL/ELT processes, data models, warehouses, analytics, and reporting solutions using AWS, Snowflake, Mage AI, and other big data tools. Collaborate with data scientists and analysts on predictive models, statistical analyses, and machine learning algorithms. Develop software with Next.js and Python while working closely with the team and clients on financial-domain challenges.

Build and evaluate machine learning models and features for fraud detection across account opening, account takeover, and identity risk. Analyze complex datasets to identify fraud patterns, develop hypotheses, validate models, and measure technical and business performance. Write tested Python code, support production deployment and monitoring, communicate findings, and follow standards for privacy, explainability, validation, documentation, and governance.

Owns P&L performance, profitability, growth, and digital transformation for Small and Lower Mid-Market commercial insurance portfolios. Leads pricing, underwriting, portfolio monitoring, analytics, data visualization, predictive modeling, and machine learning initiatives. Partners with actuarial, product, engineering, technology, and distribution teams to implement process improvements and underwriting actions across digital channels and geographies. Communicates insights to stakeholders and senior leadership while leading cross-functional teams in an Agile operating model.

Serve as the persistent technical partner for strategic and premium customers, diagnosing workflows, labeling, configurations, model behavior, and technical health. Build success plans with Customer Success Managers, guide implementations and model releases, conduct technical health reviews, coordinate fraud incident responses, and communicate technical findings to business and engineering stakeholders. Identify recurring customer patterns and provide feedback to Product and Engineering while supporting account retention and growth.

Own technical relationships with insurance carrier accounts by leading discovery, solution design, integrations, deployment support, defect management, QA, documentation, API and payload validation, SQL-based account monitoring, and incident management. The role partners with client engagement and engineering teams to ensure deployments meet carrier requirements and progressively assumes greater ownership of technical account relationships.

Provide technical pre-sales expertise for Jumio’s identity verification, KYC, AML, authentication, and risk management solutions. Responsibilities include consulting with prospects, leading technical presentations, responding to RFPs and RFIs, managing proofs of concept, onboarding customers through go-live, delivering customized web and mobile demos, and supporting sales, account management, product, engineering, and marketing teams.

Lead and grow a team of Conversation Designers to build and scale conversational AI experiences. Partner with Product, Engineering, Analytics, and ML teams to drive strategy, tooling, evaluation, and quality practices. Recruit and remove roadblocks, own strategic initiatives, and remain hands-on in designing, testing, and improving AI-driven conversational products.

Leads strategy, roadmap, execution, commercialization, and customer outcomes for a major enterprise software product area. Conducts market and customer discovery, prioritizes opportunities, partners with engineering and design, establishes outcome-based metrics, and supports sales enablement. Builds and coaches the product management team, manages executive and customer relationships, and helps transition the company from services-led to product-led operations. The role emphasizes legal technology, eDiscovery, AI-enabled workflows, enterprise data, and regulated-industry requirements.

Designs, develops, trains, tests, and deploys secure AI and machine learning solutions for enterprise and government environments. Responsibilities include preparing datasets, identifying automation opportunities, evaluating model performance and security, documenting risks and controls, and collaborating with data scientists, software developers, cybersecurity engineers, architects, and domain experts. The role applies AI and ML to enterprise security engineering, testing, and mission workflows.

Develop machine learning, AI, statistical, and predictive models using Snowflake and other data sources. Perform data extraction, cleansing, feature engineering, experimentation, visualization, and quality analysis. Partner with business and technical teams to deliver data products, conversational analytics, and actionable recommendations. Lead discovery sessions, communicate insights to nontechnical audiences, mentor analysts, and support enterprise data science projects.

Own technical delivery and account health for insurer software deployments. Responsibilities include discovery, solution design, integrations, API and payload review, QA testing, defect documentation, workflow mapping, SQL-based production monitoring, incident management, and technical communication with carrier accounts and engineering teams.

Owns customer retention and expansion across a portfolio of accounts. The Renewals Manager engages customers before renewal, drives value-based conversations, analyzes usage and health data, identifies churn and expansion opportunities, and coordinates renewal execution across Sales, Finance, and Legal. The role also develops scalable engagement frameworks, presents pipeline insights to leadership, and partners with Account Executives and technical customer success teams in a B2B technology environment.