Top Remote Machine Learning Jobs in Providence, RI

Lead the creation and optimization of machine learning algorithms, develop and refine production-grade models, and drive testing of innovative algorithms using historical data.

The Reservoir Engineer will support development optimization, production forecasting, and data-driven decision making to maximize asset value.

Lead strategy, roadmap, and delivery for Tekmetric's SaaS product for auto repair shops. Own user research, AI/ML product initiatives, cross-functional alignment, go-to-market coordination, and measurement of business outcomes. Train internal teams and iterate on features from concept through launch and optimization.

Lead a cross-functional team to define and execute the product roadmap for Capacity Management and AI Virtual Agents. Own product lifecycle from research through launch, set KPIs for revenue and adoption, drive integrations via APIs, and apply ML/AI/IoT opportunities. Collaborate with R&D, go-to-market, and strategic partners to improve product-market fit and expand addressable market while resolving escalations and optimizing customer outcomes.

Lead and operate a 10,000 sq ft micro-fulfillment lab to validate and scale warehouse automation, robotics, and AI-driven operations. Own strategy, budgeting, vendor selection, experimentation, and deployment frameworks to drive measurable productivity, capacity, accuracy, and cost improvements across Stord's fulfillment network. Build and mentor a multidisciplinary team and partner with internal and external stakeholders to commercialize solutions.

Lead the development of deep learning models for email data, optimize models, design evaluation frameworks, integrate products, and mentor junior researchers.

Lead onboarding and training for new customers of an AI-powered construction SaaS. Customize training, run webinars and office hours, create documentation, drive user adoption, surface issues with support, and collaborate with product and customer success to refine onboarding processes.

Lead product discovery, definition, and delivery for TraceGains platform capabilities with emphasis on AI-enabled features. Collaborate cross-functionally to prioritize backlog, drive go-to-market and adoption, define success metrics, and take products from concept to launch and scale while measuring impact across adoption, retention, efficiency, and revenue.

Design and build reinforcement-learning training environments and diverse tasks to evaluate and improve LLM agents; iterate rapidly on task designs from customer feedback, deliver high-quality outputs with minimal supervision, and maintain PST overlap for collaboration.

Lead product strategy and execution for data and AI platforms, driving GenAI/LLM initiatives, cross-functional collaboration, responsible AI governance, experimentation (A/B testing), measurement of model impact, and mentoring product teams to embed scalable ML capabilities into clinical and member experiences.

Lead the development and implementation of machine learning solutions, managing a team to drive strategic decisions and innovative business outcomes.

The Sales Executive will develop new AI and ML service opportunities, manage sales pipelines, and engage with technical decision-makers primarily in the US and Canada. Responsibilities include proposal shaping, pipeline management, and client relationship management with a focus on complex technology sales.

The Machine Learning Engineer will design, build, and maintain scalable ML systems, optimize ML pipelines, implement MLOps, and collaborate with teams to deploy models into real-time products.

The Staff Machine Learning Engineer will develop and maintain MLOps tools, optimize model performance, and collaborate on ML system architecture with a focus on enhancing developer experience.

The role involves improving machine learning ranking models, implementing algorithms, and taking full ownership of machine learning systems within Quora's product.

Design, train, and deploy ML models for Qur'ān recitation, tajwīd detection, and ASR. Build data pipelines for cleaning, segmentation, labeling, and evaluation. Integrate real-time predictions via APIs or lightweight on-device inference, optimize models for speed and mobile performance, and collaborate with backend developers to ship AI features in the app.

Develop and tune ML models, productionize and deploy strategies for ML systems, and align cross-functionally to communicate performance metrics.

As a Staff Machine Learning Engineer, you will develop, productionize, and deploy ML models for clinical use, ensuring real-world effectiveness through hands-on coding and infrastructure building.

Lead GoDaddy's three-year Generative AI and ML roadmap, architecting and scaling agentic and personalized AI solutions globally. Own end-to-end ML systems, enable rapid experimentation, ensure governance/compliance, and partner across product, engineering, and analytics. Mentor and grow high-performing teams and drive adoption of advanced AI techniques to improve customer experiences and business outcomes.

Design and implement infrastructure to support generative AI and machine learning, optimizing workflows and integrating new technologies while ensuring reliability and performance.

Serve as an AI/ML-focused solution engineer for Healthcare & Life Sciences: provide technical guidance, design cloud data and lakehouse architectures on Snowflake, lead demos and proofs-of-concept, support sales cycles, collaborate with product and engineering, and communicate AI/ML value to technical and executive stakeholders.

As a Staff Machine Learning Engineer at Twilio, you will build and maintain data pipelines and ML infrastructure, ensuring high-quality data and model performance while collaborating with product and security teams.

The ML Engineer will leverage deep learning for natural language processing tasks, impacting Entefy's products while requiring strong machine learning fundamentals and NLP expertise.

Build and maintain C#/.NET automation tooling to onboard and standardize industrial telemetry. Develop and refine applied ML classification models mapping telemetry tags to templates, perform feature engineering on messy historical datasets, validate end-to-end data pipelines (Azure IoT, Event Hub, Kusto), modernize legacy systems, troubleshoot production issues, document solutions, and mentor teammates to improve automation and reduce manual onboarding effort.

Lead the development of generative AI tools for fashion, guiding ML engineers, and implementing innovative AI systems and workflows.