Top Remote Machine Learning Jobs in Boise, ID

Researches, develops, tests, and maintains materials-aware machine learning models and software. Designs scalable ML systems, improves model interpretability and uncertainty quantification, and develops inverse-design capabilities. Collaborates with product, engineering, and external research teams to deliver customer-focused AI solutions, publishes research, reviews code, analyzes model performance, and mentors developers.

Develop and deploy machine learning algorithms that improve real-time device identification and fraud detection. Own data science projects from experimentation through production, including supervised, semi-supervised, and unsupervised learning, real-time inference, training automation, exploratory analysis, and model-to-service integration. Contribute to engineering best practices, collaborate within a global remote team, and participate in a shared on-call rotation.

Improve 1Password’s technical SEO across consumer, PLG, and enterprise websites. Responsibilities include diagnosing and resolving crawlability, indexing, rendering, performance, structured data, internal linking, and international SEO issues; implementing CMS changes; partnering with engineering on requirements and release QA; leading migration workstreams; and building monitoring, dashboards, automation, and repeatable processes using search, analytics, crawl, log, and CMS data.

Own product strategy and delivery for agentic document management, policy and procedure workflows, and compliance gap assessments. Partner with customers, design, engineering, and go-to-market teams to build reliable, explainable AI experiences that accelerate audit readiness and security program expansion. Responsibilities include defining product requirements, evaluating AI outputs, improving quality, synthesizing customer feedback, prioritizing roadmaps, and shaping product culture.

Own the vision, strategy, roadmap, discovery, delivery, and continuous improvement of Rezilient’s member experience products. Partner with clinical operations, engineering, design, customer success, and sales to ship accessible, engaging healthcare experiences. Define requirements, prioritize backlogs, establish product metrics, ensure healthcare compliance, and help integrate AI into care delivery. The role requires experience scaling consumer health products, building AI or machine learning features, and operating effectively in a regulated startup environment.

Provides strategic legal counsel on clinical trials, commercial biopharma services, healthcare regulation, privacy, data protection, and EU employment matters. Advises on GDPR, HIPAA, clinical data use, cross-border transfers, privacy impact assessments, contracts, workforce initiatives, works councils, and regulatory developments. Partners with clinical, commercial, HR, compliance, technology, and senior leadership teams while supporting privacy program implementation and risk management.

Leads credit risk analytics for Prosper’s Credit Card business, overseeing underwriting, portfolio, pricing, credit line, fraud, and credit risk strategies. Uses machine learning, AI, and advanced analytics to manage growth while controlling losses within risk appetite. Presents recommendations to executives, the Credit Risk Committee, and investors; monitors performance and emerging trends; evaluates vendors and alternative data; and collaborates across Capital Markets, Product, Engineering, Legal, Compliance, Operations, and Marketing. Also builds and mentors high-performing credit risk teams.

Own and grow strategic Financial Services accounts through executive relationship management, consultative selling, renewals, and expansion. Lead commercial strategy, SOWs, negotiations, QBRs, delivery governance, escalations, and value realization. Coordinate delivery, sales, solutions, cloud, and Databricks partners while managing scope, budgets, timelines, margins, retention, profitability, and customer satisfaction. Translate Databricks, data engineering, analytics, ML, AI, and cloud transformation capabilities into measurable business outcomes.

Leads regional life science enterprise sales by developing pipeline, closing complex deals, expanding accounts, and building executive relationships across health plans, health systems, biotech, and pharmaceutical organizations. The role requires consultative selling of AI/ML healthcare solutions, cross-functional collaboration, CRM management, contract negotiation, and trusted-advisor engagement with C-level decision-makers. This remote position requires 25–50% travel.

Drive enterprise sales of Snowflake’s AI and machine learning solutions by generating pipeline, achieving revenue targets, developing territory strategies, and executing tailored sales plays. Engage executives and technical decision-makers, manage complex sales cycles, collaborate with account executives, sales engineers, field CTOs, and cross-functional teams, and provide customer insights to inform product strategy. Track sales performance and guide customers through AI/ML solution adoption, implementation, and deployment.

DoD SkillBridge Fellows gain practical experience through assigned projects, collaboration with experienced professionals, and exposure to operational processes, technologies, and mission objectives. Opportunities span telecommunications, acquisition and procurement, technology, accounting, finance, and other areas. Participants are compensated by their military employer during the fellowship and may be considered for full-time employment afterward based on performance and organizational needs.

Own the technical side of deal progression for airline deployments: qualify opportunities, run demos and POCs, author integration and deployment architectures (PSS, APIs, data feeds, DNS, identity, networking), respond to RFPs/security questionnaires, scope SOWs and NFRs, and build reusable demo/reference assets to drive adoption and expansion while liaising between sales, engineering, and customers.

Lead data science projects from ideation through enterprise impact, building scalable and well-tested solutions that improve healthcare outcomes. Apply probability, statistics, machine learning, SQL, and Python to develop data-driven products and insights. Partner with stakeholders, assess product performance, identify strategic improvements, mitigate risks, and mentor teammates while aligning initiatives with business objectives.

Own end-to-end strategy, roadmap, delivery, adoption, safety, and commercial readiness for AI-native cybersecurity products. Conduct customer and partner discovery, lead 0-to-1 launches, define experimentation and evaluation frameworks, and collaborate with engineering, design, sales, finance, and executives. The role requires hands-on LLM product expertise, strong judgment in ambiguous environments, and a focus on trusted AI experiences for security professionals and MSP/MSSP partners.

Build and deploy AI-driven process optimization models for refineries and petrochemical plants. Scope customer opportunities, analyze plant time-series data, develop and validate process models, commission closed-loop optimizers, train operators, troubleshoot deployed models, and support plant-system integrations. The role also leads technical customer relationships, manages implementation projects, documents workflows, and channels field feedback to product and R&D teams.

Develop and deploy AI solutions from problem framing through production. Responsibilities include identifying AI opportunities, leading MVP development, building data pipelines, experimenting with generative LLMs, fine-tuning NLP models, and delivering scalable features. The intern will collaborate cross-functionally, communicate technical impact to business audiences, and work with cloud platforms such as AWS or Google Cloud.

Support partner sales opportunities through discovery calls, product demonstrations, technical responses, solution scoping, project planning, proof-of-concept coordination, RFI/RFP responses, and security questionnaires. Collaborate with sales, professional services, product, engineering, and delivery teams while building partner relationships and developing expertise in fraud prevention and financial crime solutions.

Lead end-to-end product design for an AI-powered women’s health platform. Responsibilities include defining UX vision, creating interaction models, information architecture, wireframes, high-fidelity interfaces, prototypes, design systems, and user research. Collaborate with product, engineering, ML, marketing, and leadership teams to translate business needs into customer-focused solutions, manage design initiatives, and measure product success through user feedback and metrics.

Lead Nabla’s marketing organization and product marketing strategy across healthcare segments, buyer types, and direct and partner channels. Own positioning, messaging, launches, competitive intelligence, sales enablement, demand strategy, content prioritization, marketing operations, pipeline attribution, and team development. Manage a 6–8-person team spanning product marketing, growth content, design, and marketing operations while guiding resourcing and hiring decisions.

Leads the transformation of Sophos’s pricing operations by building a centralized function, establishing pricing governance and review cadences, developing AI-assisted competitive and discount analysis capabilities, and aligning pricing across SecOps and Network Security. The role provides executive market visibility, defines performance metrics, supports organizational change, and builds a high-performing team serving sales, finance, and customer-facing stakeholders.

The Tarbell Fellowship combines three months of remote training in AI and journalism fundamentals with a nine-month newsroom placement as an artificial intelligence reporter. Fellows write and investigate AI-related topics, build journalism skills, complete coursework and assignments, and attend a weeklong in-person summit. The program is full-time and intended for candidates with journalism or AI expertise, strong writing ability, persistence, curiosity, and open-mindedness. Senior fellowships are available to candidates with at least seven years of experience and exceptional prior work.

Own the strategy, roadmap, and investment priorities for foundational agricultural data products, including pipelines, data models, APIs, and shared services. Partner with engineering, architecture, data science, and product teams to deliver scalable, governed, high-quality data capabilities. Prioritize initiatives based on customer and commercial value, define success metrics, resolve cross-team tradeoffs, and expand data support for analytics, predictive modeling, machine learning, and AI applications.

Lead end-to-end LLM inference performance across serving systems, models, GPU hardware, and workloads. Define rigorous benchmarks, diagnose bottlenecks from scheduling through kernels and interconnects, optimize single- and multi-node deployments, and deliver production-ready runtimes and configurations. Collaborate with product and infrastructure teams, evaluate inference technologies, and implement runtime fixes. The role requires strong Python engineering, production experience with serving engines such as vLLM or SGLang, GPU profiling, and knowledge of modern inference optimization techniques.

Build and deploy production machine learning systems that optimize Samsara’s global hardware supply chain. Responsibilities include demand, inventory, lead-time, spend, and supplier-risk forecasting; anomaly detection; feature engineering and ETL; real-time inference; MLOps; data infrastructure improvements; governance; and model monitoring. The role defines the AI transformation roadmap, partners with executive and cross-functional stakeholders, mentors scientists, and drives adoption of AI-enabled operational workflows.

Lead client engagements from data onboarding through production, plan studies that demonstrate business value, and build automated machine learning workflows using Python, scikit-learn, pandas, and Spark. Translate client business questions into data solutions, manage stakeholder relationships, evaluate model performance, and inform product planning. Requires healthcare or life sciences data experience and end-to-end delivery of sophisticated data or machine learning solutions.