Top Machine Learning Jobs in Providence, RI

Lead vision, strategy, and execution for Alkami's marketing products. Define roadmap, embed AI-driven personalization and audience intelligence, partner cross-functionally to drive adoption and revenue, recruit and develop product managers, and deliver scalable SaaS marketing and analytics solutions for financial institutions.

Design and implement machine learning models to solve business problems, analyze data for insights, and optimize model performance.

Lead a team of safety engineers to define and execute AV safety strategy across development, simulation, testing, and deployment. Apply UL4600/ISO standards, develop safety cases, set safety metrics (SPI), manage safety plans and artifacts, train engineers, and coordinate with partners and stakeholders to validate ML models and ADS integration for safe public-road operation.

Lead technical pre-sales for large Oil, Gas & Energy accounts, define and implement Databricks-based data and ML/AI strategies, build technical champions, mentor teammates, and drive enterprise adoption through technical leadership and value selling.

Lead strategy and roadmap for Brain Health & Detection, drive AI/ML-powered detection and anomaly systems, partner with engineering and data science, set success metrics, and lead end-to-end product execution and adoption for scalable service health intelligence.

Conduct research on AI tokenomics by evaluating model architecture trends, building simulation models for training and inference efficiency across hardware and workloads, diligencing companies using model tokens, and forecasting economic impacts for investors, corporates, and policymakers.

Manages client relationships and technology and analytics project delivery. Advises data science and engineering teams on business analysis, technology approaches, project scope, schedules, costs, risks, and quality assurance. Develops client engagement strategies, technology roadmaps, proposals, statements of work, and pricing. Partners with executives to identify portfolio growth opportunities and communicates the business value of analytics, statistics, and machine learning.

The Senior Financial Analyst partners with Product Delivery leaders on financial planning, forecasting, reporting, variance analysis, investment evaluation, and resource allocation. The role develops financial models, manages acquisition spend analysis, supports headcount planning, enhances Anaplan, and delivers executive-ready insights. Responsibilities also include evaluating strategic initiatives, improving FP&A processes, and advising cross-functional leaders on risks, opportunities, tradeoffs, and financial performance.

Design and implement machine learning and optimization algorithms for Netflix’s ad marketplace. Build, train, and evaluate models using large-scale production data; develop online and offline evaluation frameworks; and partner with product teams to define objectives and trade-offs. Communicate technical decisions and experiment results to diverse stakeholders while providing technical guidance and aligning ML solutions with business goals.

Leads enterprise sales strategy and revenue growth for Epiq’s AI-powered eDiscovery, legal technology, compliance, analytics, and information governance solutions. Develops pipelines, manages complex consultative sales cycles, expands strategic accounts, and engages executive stakeholders across corporations, law firms, government, and regulated industries. Acts as an AI and legal technology subject matter expert, supports responsible AI adoption, partners cross-functionally on solution development and delivery, and represents Epiq through market development, thought leadership, and strategic partnerships.

Lead technical strategy and architecture for Azure Storage geo-replication, data protection, failover orchestration, and business continuity. Drive cross-team delivery, operational excellence, telemetry, automation, AI-assisted operations, and represent the team in executive and customer engagements.

Design and operate large-scale, AI-native cloud infrastructure (especially Kubernetes/AKS). Improve service quality, automate incident prevention/remediation, accelerate engineering productivity, and integrate AI tools into development workflows.

Drive go-to-market execution across Nabla’s product portfolio, including positioning, messaging, sales enablement, competitive intelligence, demand-generation partnerships, customer adoption content, and event support. Progress into owning go-to-market strategy for specific products from early definition through launch and adoption, partnering with Product and Engineering and tracking launch success metrics.

Own credit portfolio performance analysis, credit policy, underwriting strategy, risk frameworks, and portfolio growth initiatives across lending products. Partner with Data Science, Product, Engineering, Operations, leadership, and external lending partners to develop predictive models, automated decisioning, analytical tools, dashboards, and reporting. Present strategic recommendations on portfolio risk, borrower behavior, credit expansion, and company-wide risk strategy.

The Sr. Machine Learning Engineer will develop and deploy ML solutions for healthcare, manage data pipelines, and work with large datasets to enhance healthcare delivery.

As a Staff Applied ML Scientist, you will develop and maintain fraud detection models, conduct research on fraud types, and collaborate across teams to drive product development and real-time decision-making.

As a Senior Applied ML Scientist, you'll develop fraud detection models, research new fraud types, and write production code while collaborating across teams to enhance SentiLink's identity verification solutions.

Lead end-to-end healthcare analytics projects, managing scope, planning, execution, client relationships, cross-functional collaboration, risks, and delivery. Provide thought leadership in healthcare analytics and identify business opportunities within client accounts. Required capabilities include quantitative analysis, project management, communication, presentation, SQL/Python/R proficiency, and strategic use of AI tools. Preferred experience includes consulting, cloud analytics platforms, healthcare data regulations, predictive modeling, machine learning, and data visualization.

Leads architecture and development of production-grade mapping and localization ML systems for autonomous vehicles. Designs neural models for detection, segmentation, tracking, depth estimation, 3D reconstruction, and semantic mapping; owns data strategy, evaluation, deployment, and fleet validation. Mentors engineers, drives technical roadmaps, collaborates cross-functionally, and advances vectorized HD mapping, foundation models, and real-time optimization.

Own end-to-end design for complex financial product experiences from exploration through launch and iteration. Translate financial workflows into intuitive customer experiences, set high standards for interaction and visual design, partner with product and engineering, use research and data to guide decisions, contribute to design systems, present work clearly, and mentor teammates. Apply AI-enabled tools thoughtfully while maintaining strong human-centered judgment.

Lead implementations of iQueue for Operating Rooms from discovery through configuration, validation, and rollout. Partner with hospital, clinical, operational, IT, and executive stakeholders to optimize OR workflows, scheduling, staffing, utilization, and access. Responsibilities include data analysis, solution design, product configuration, user acceptance testing, change management, risk monitoring, stakeholder alignment, and go-live readiness. Travel to customer sites is required up to 40–50%.

Design and implement scalable machine learning pipelines for insurance use cases, including classical ML and LLM workloads. Lead production deployments, build reusable ML infrastructure and CI/CD pipelines, and establish observability, testing, model monitoring, drift detection, logging, and rollback standards. Mentor junior engineers, collaborate cross-functionally, manage cloud-based ML systems, and communicate technical findings to nontechnical stakeholders while maintaining insurance data compliance.

Lead initiatives in designing and building large-scale distributed data systems and ML infrastructure, while collaborating across various engineering teams.

Design, build, and ship production AI agents integrated end-to-end into a TypeScript/Node.js/React stack. Apply ML judgment to product decisions, improve agent reliability and evaluations, and move quickly in a startup environment owning ambiguous problems.

Leads an engineering team building and scaling customer-facing integrations for agentic AI products. Responsibilities include setting technical direction, balancing customer delivery with reusable tooling and platform investments, ensuring reliability and observability, improving developer experience, collaborating cross-functionally, recruiting, and coaching engineers. The role requires customer empathy, strong engineering leadership, technical problem-solving, and experience with production systems or integrations.