Top Senior Machine Learning Jobs in Providence, RI

Serve as the technical lead in pre-sales for state, local, and education customers: run discovery, design POCs and workshops, deliver demos, map needs to Socure solutions, advise on fraud/risk/compliance, influence product, and mentor junior consultants.

Lead and grow the AI/ML Engineering team, manage core AI/ML platform development, ensure production-grade service operational excellence, and develop product strategies.

The Engineering Manager will lead teams in developing and managing Blackbaud's Data & AI platform, focusing on scalability, security, and cost-efficiency.

The role involves developing and integrating AI features into Cribl's products, collaborating with teams, and maintaining operational efficiency.

Own end-to-end ML lifecycle for pricing and underwriting: improve model accuracy and coverage, reduce infrastructure cost, deploy and monitor production models on AWS, run experiments/backtests, and collaborate with domain experts to inform model improvements and product APIs.

Lead MLOps for production ML lifecycle: deploy and serve models, build scalable reproducible pipelines for large protein-design workloads, implement CI/CD and observability, optimize GPU/accelerated compute, and enable self-service tooling for researchers.

Design, build, and productionize ML and AI systems (LLMs, embeddings, recommendations, forecasting). Develop end-to-end ML workflows, scalable data pipelines, semantic models, and CI/CD for ML. Collaborate with product and engineering, mentor engineers, and influence platform architecture and standards.

Lead development of digital twin and simulation models for warehouse operations, deploy AI-driven decision systems, build forecasting and optimization models, validate experiments linking operational metrics to financial impact, and partner with engineers, operations, and external AI/academic partners to scale innovations across fulfillment networks.

Lead end-to-end delivery of LOIS by owning the master program plan, coordinating cross-functional teams (engineering, product, design, ML, data, SRE, operations), resolving blockers and dependencies, establishing lightweight delivery infrastructure and tooling, partnering on release planning, and maintaining transparent status, risks, and accountability to deliver complex features to customers.

Lead fraud detection and mitigation for a conversational BNPL product. Define fraud typologies, own repayment and chargeback analytics, manage fraud KPIs, design operational policies, collaborate on ML models and decisioning workflows, and coordinate SARs and compliance reporting.

Lead credit analytics for the Send Now Pay Later product: build and maintain credit policies, pricing models, vintage and NPV analyses, executive reporting, model integration and testing, and explore alternative data to optimize approval rates and portfolio health.

Lead fraud detection and mitigation for a conversational Send Now Pay Later product: define first-party fraud typologies, own repayment and chargeback analytics, manage fraud KPIs, design operational policies, integrate ML and decisioning tools (Taktile), and coordinate SARs and compliance reporting.

Lead credit analytics for the Send Now Pay Later product: build and optimize valuation, vintage and NPV models; manage credit policies, portfolio monitoring, and NPL reporting; integrate and test scoring models with engineering/data science; and research alternative data to improve predictive performance.

Lead fraud detection and mitigation for a conversational BNPL product: define fraud typologies, analyze repayment/chargeback trends, own fraud KPIs, design policies, collaborate on ML models and decisioning workflows, and coordinate SARs with Compliance.

Lead fraud risk for a conversational lending product (SNPL): define first-party fraud typologies, analyze repayment and chargeback trends, own fraud KPIs, design automated/manual blocking policies, coordinate ML/rules with central Fraud and Data teams, and liaise with Compliance on SARs and loan abuse.

Lead quantitative credit analytics for a Send Now Pay Later (SNPL) product: build and maintain credit policies, pricing and NPV models, vintage analysis, portfolio monitoring, model integration/testing, and alternative data evaluation to optimize approval rates and manage portfolio health.

The Engineering Manager for Smart Signals leads a team, manages execution, and maintains technical depth in backend systems for fraud detection. They shape strategy and coordinate across teams to enhance product quality.

The Senior Backend Data Engineer will extend data ingestion and processing pipelines, build APIs and backend services, and provide technical leadership.

Design, optimize, and scale ClickUp's search infrastructure using OpenSearch/ElasticSearch. Improve relevance, speed, and real-time indexing; build vector/semantic search capabilities; create measurement frameworks; integrate search with AI and product features; troubleshoot at scale; and mentor junior engineers to maintain high search quality under heavy load.

Design, scale, and optimize ClickUp's search infrastructure using OpenSearch/Elasticsearch. Improve relevance, speed, and real-time indexing, build vector search capabilities, create evaluation frameworks, and collaborate with AI/backend/product teams to integrate and troubleshoot search at scale.

The Senior AI Data Engineer will design and build scalable data pipelines, ensure data quality, and support AI product development through a unified analytical data framework.

The Principal Data Scientist will design, develop, and deploy machine learning models, innovating methodologies and collaborating across teams to enhance financial decision-making and risk management.

Lead technical pre-sales and enterprise rollouts by owning infrastructure strategy, PoCs, migrations (on‑prem to cloud), DMS/CRM integrations, InfoSec discussions, and stakeholder engagement with C-level sponsors to enable scalable deployments and upsell opportunities.

As a Senior Data Scientist, you will identify business opportunities, build machine learning solutions, partner cross-functionally, and translate analyses into actionable insights.

The Senior Manager will oversee the fraud lifecycle, optimize detection strategies, conduct fraud reviews, collaborate with teams, and manage risk reporting.