Top Remote Machine Learning Jobs in Providence, RI

Research and improve real-time conversational AI for voice drive-thru ordering. Responsibilities include advancing speech, intent, turn-taking, voice activity detection, and order-accuracy models; curating and generating data for rare scenarios; building statistically rigorous benchmarks; evaluating audio and conversational quality under real-time constraints; and translating research into production improvements with applied AI and backend teams.

Own and improve the end-to-end AI engine powering real-time voice drive-thru agents. Design conversational and order-taking logic, address turn-taking and noisy audio challenges, build evaluation infrastructure, and ensure accurate recommendations, substitutions, pricing, and POS integrations across restaurant brands. The role involves researching and deploying new AI techniques, training and productionizing machine learning models, and converting ambiguous quality issues into measurable improvements.

Tune AML and fraud detection models using customers’ live transaction data. Investigate missed cases, alert quality, pattern drift, and false positives; validate changes through backtesting and sample review; document regulatory rationale; and present model performance and business value to compliance, risk, and data science stakeholders. Partner with customer teams on account strategy and relay customer-specific or systemic findings to product and model functions.

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.

Reviews and evaluates engineering, infrastructure, and platform documents and work products for technical accuracy, completeness, clarity, consistency, and relevance. Provides structured feedback, identifies errors and gaps, compares technical approaches, and follows defined evaluation frameworks. Requires independent work, strong communication, analytical ability, and professional engineering judgment. The role is fully remote and supports varied technical evaluation projects as an independent contractor.

Drive net-new enterprise customer acquisition from lead generation through contract closure. Build C-level relationships, develop account strategies, manage CRM pipelines and forecasts, negotiate complex contracts, and close large AI, machine learning, and data talent engagements. Partner with marketing, customer success, and talent teams to deliver customer outcomes and expand Factored AI’s enterprise footprint.

Consult as a fractional data scientist for hedge funds, private equity firms, and family offices. Lead data science initiatives: build infrastructure, develop and deploy models, create portfolio dashboards, monitor post-deployment performance, and partner with investment teams to turn data into actionable investment insights. Advise on vendor selection, data quality, technology stacks, KPIs, and governance.

Design and deploy production-ready LLM pipelines and agentic workflows, automate large-scale multimodal dataset pipelines, monitor and improve agent performance, and integrate AI agents with 3D scene composition. The role also involves process documentation, incident response, and continuous improvement, with collaboration across the engine team and company founders.

Designs, develops, and deploys AI-driven applications and workflows on ServiceNow. Gathers requirements, integrates machine learning, NLP, predictive analytics, and external systems, and manages application coding, testing, debugging, documentation, deployment, and incident remediation. Implements ServiceNow modules, CMDB, Discovery, workflows, service maps, and event management while collaborating with stakeholders and technical leads. The role also includes mentoring engineers, supporting complex professional services projects, and maintaining platform standards.

Leads technical discovery, enterprise solution architecture, technology assessments, and implementation roadmaps for consulting clients. Designs architectures across applications, data, cloud, integration, and enterprise technology; advises executives on technology decisions, scalability, security, cost, and risk. Guides technical teams, resolves complex delivery challenges, develops proofs of concept and reusable standards, evaluates emerging technologies, and supports proposals, estimates, statements of work, and new business development.

Own end-to-end analytics delivery for a client POD, translating business priorities into workplans, coordinating cross-functional teams, governing delivery and revenue KPIs, and managing risks through resolution. Perform hands-on data profiling, validation, reconciliation, exploratory analysis, automation, and quality reviews using SQL, Python, Databricks, and Excel. Create executive-ready KPI, revenue, and business-impact narratives, prioritize initiatives, support business cases, and identify opportunities for analytics automation and AI-enabled improvements.

Lead development of Root’s machine learning platform for insurance pricing. Build and operate feature pipelines, model training and orchestration, registries, validation, serving, observability, and reproducibility tooling. Partner with researchers to convert experimentation into reliable production systems, explore LLM-enabled data science automation, drive technical roadmaps and execution, and mentor engineers while maintaining high standards for reliability and correctness.

Lead the architecture and development of Root’s machine learning platform for insurance pricing. Build infrastructure for feature pipelines and stores, reproducible training and orchestration, model registries, serving, validation, observability, and research-to-production workflows. Establish versioned contracts and technical standards, automate workflows using LLMs, write and review critical code, influence cross-team strategy, and mentor senior engineers. Collaborate closely with data scientists and researchers to create scalable, reliable, and reproducible ML capabilities.

Coordinates data platform incident response, triage, escalation, root cause analysis, problem management, post-incident reviews, and operational improvements. Maintains runbooks, documentation, metrics, dashboards, and stakeholder communications. Implements and monitors security controls, supports security incident response, vulnerability remediation, compliance audits, regulatory reporting, disaster recovery exercises, and cross-functional coordination across data, cloud, infrastructure, DevSecOps, application, and governance teams.

Develop and deploy predictive and causal models for personalization, retention, monetization, and product strategy. Design experimentation and measurement frameworks, analyze user and marketing behavior, quantify business impact, and translate complex findings into strategic decisions. Collaborate with Product, Engineering, Marketing, and Data Product teams to build scalable data solutions. Mentor peers, promote rigorous model validation and governance, and apply Python, SQL, Snowflake, dbt, Airflow, Spark, and AWS to production-ready data science initiatives.

Lead the design and implementation of large-scale AI and machine learning systems for Yahoo Search across multiple teams. Define data strategies, evaluation frameworks, and scalable production-serving approaches. Drive technical alignment among engineering, product, design, and research teams; guide adoption of large language models, vision models, generative AI, and agentic AI; and oversee data analysis informing strategic product decisions.

Develop threat models, statistical verification algorithms, calibrated tests, and production verification services for decentralized training and inference on untrusted heterogeneous hardware. The role focuses on detecting incorrect model outputs, malicious training behavior, free-riding, poisoning, data extraction, and reward manipulation while controlling false positives and negatives. Candidates need deep statistics and probability expertise, experience shipping or publishing calibrated decision systems, and familiarity with statistical testing, re-execution, cryptographic proofs, and trusted hardware.

Provides clinical expertise, customer support, product demonstrations, training, onboarding, and certification for Transpara Breast AI across the USA and Canada. Supports research initiatives, clinical adoption, customer engagement, NPS tracking, workshops, webinars, educational content, case curation, and customer feedback resolution. Collaborates with Sales, Customer Success, Marketing, Engineering, Innovation, and Product Management teams. Requires medical imaging experience, preferably in mammography or breast imaging AI, strong communication and training skills, and approximately 20% travel.

Drive enterprise sales growth across North American banking, credit card, and financial services accounts. Develop strategic account and business development plans, build customer and partner relationships, close complex multi-million-dollar technology deals, and engage C-level stakeholders. Represent the company at industry events, collaborate with commercial and leadership teams, manage pipeline forecasting and reporting, and apply MEDDPICC methodology. The role focuses on cybersecurity, fraud prevention, authentication, and SaaS or cloud solutions, with 50% domestic and international travel.

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.

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 the design, development, deployment, and governance of advanced analytics, machine learning, and AI solutions using large healthcare datasets. Responsibilities include data preparation, statistical and predictive modeling, model monitoring, explainability, visualization, stakeholder communication, and translating findings into clinical and operational improvements. The role also mentors data professionals, advises leaders, manages initiative priorities and risks, and promotes responsible AI and Agile practices.

You will tackle AI challenges, develop scalable software and collaborate with teams to enhance healthcare navigation using AI solutions.

Lead and transform the Fraud Intelligence Team into a high-impact function by improving operating models, QA, tooling, and metrics; build entry-level talent programs; connect labeling to measurable fraud prevention outcomes; deliver actionable insights to Product, R&D, Analytics, and GTM; and leverage AI and automation to increase productivity and business impact.

Partner with functional leaders to translate business strategy into people strategy. Own org design, performance management, compensation, talent development, engagement, and retention. Provide coaching on career pathing, change management, and sensitive employee issues. Use people data to generate insights and recommendations, build compliant HR policies, manage global employee relations, and collaborate with People Team COEs to deliver cohesive people programs at scale.