Top Machine Learning Jobs in Hartford, CT

Build and deploy machine learning models for credit and fraud risk, using large-scale financial data to improve lending decisions and business performance. Monitor production models, validate algorithms and features, conduct risk analyses, follow regulatory and responsible AI practices, and collaborate with engineers and cross-functional stakeholders. Develop productivity tools, document models, and communicate technical findings to senior management.

Own the strategy, vision, roadmap, and execution for Proof’s Defend anti-fraud product. Lead fraud investigation and risk prediction tools, customer discovery, strategic planning, GTM, stakeholder alignment, and executive communications. Collaborate with engineering, data science, design, fraud operations, sales, and customer success to define technical requirements, prioritize development, improve onboarding, respond to fraud cases, and deliver data-driven identity and transaction protection solutions.

Build product enablement for global go-to-market teams across AI Agents, APIs, and Creative products. Responsibilities include preparing launch workshops, competitive training, product certifications, onboarding and everboarding content, hands-on demos, role-play simulations, practice environments, and performance measurement. The role partners with product marketing, engineering, research, and enablement teams while using ElevenLabs’ AI technology to create training experiences.

Build and operate a scalable LLM inference platform integrated with the MAX stack. Develop distributed, multi-node model deployments, high-throughput batching, KV-cache management, autoscaling, observability, cold-start optimizations, and multi-cloud capabilities. Create Helm charts and Kubernetes operators while collaborating with kernel and generative AI teams to optimize performance. The role requires backend engineering, Kubernetes operations, machine learning experience, and distributed systems expertise.

Analyzes large-scale telemetry and datasets to identify content delivery performance and reliability issues. Builds software tools to automate analytical workflows and enable autonomous diagnosis. Applies statistical analysis, machine learning, networking, and distributed-systems expertise to develop targeted platform improvements. Defines and mines internal data feeds, establishes performance indicators, and partners with Engineering, Operations, and Product teams to guide feature and process adoption.

Build and improve machine learning models that identify healthcare claims errors and fraud for audit. Responsibilities include curating labeled data, engineering features, selecting optimization metrics, measuring model and business impact, refining large-scale data pipelines, and developing modeling infrastructure and team tools. The role also involves collaborating with clients and healthcare experts to align insights with business needs.

Supports clinical operations through data entry, validation, reconciliation, reporting, system maintenance, and data quality checks. Collects and transforms datasets, develops visualizations and analytical models, supports patient recruitment tracking, troubleshoots routine system issues, and maintains documentation. Collaborates with Data Management, Clinical Operations, and Patient Recruitment teams while protecting confidential information.

Own the end-to-end mobile identity verification product across SDKs, web flows, and APIs. Lead roadmap, UX, biometric intelligence, deepfake detection, liveness technology, conversion optimization, fraud prevention, and global scalability. Partner with engineering, research, design, customer success, and commercial teams to deliver scalable offerings, support customer adoption, identify upsell opportunities, and use analytics and experimentation to improve performance.

Develops data science, machine learning, and generative AI solutions for customer engagements and pre-sales opportunities. Translates ambiguous business challenges into analytical use cases, prototypes, hypotheses, demonstrations, and actionable recommendations. Explores customer data, applies statistical and advanced analytical techniques, communicates findings to technical and business audiences, and creates reusable analytics assets. Partners across Microsoft teams to connect customer data and AI opportunities with broader transformation priorities while applying responsible AI, security, governance, and data quality principles.

Conduct machine learning and data science research for drug discovery, focusing on mass spectrometry, computational biology, and computational chemistry. Own projects from scientific question through results, develop data-driven solutions, validate AI-generated analyses, and communicate findings through visualizations and writing. Collaborate with the Data Science team and contribute to applications of AI in medicine development.

Build and deploy production machine-learning models for member identification, personalization, provider search, and healthcare AI tools. Lead projects from problem framing and prototyping through product handoff, design experimentation and causal analysis, define product metrics, and develop reliable feature pipelines and experiment infrastructure. Partner with product, engineering, care operations, marketing, and analytics engineering while communicating recommendations clearly and ensuring models are monitored, tested, and impactful.

Senior Product Manager responsible for the vision, strategy, and roadmap of Root’s pricing platforms and rating engine. The role leads cross-functional prioritization across engineering, data science, analytics, actuarial, risk, legal, compliance, and state management. Responsibilities include accelerating model deployment, improving data services, building experimentation tools, governing APIs and event-driven architectures, reducing regulatory bottlenecks, and supporting expansion into new states and markets.

Develop and integrate production AI/ML capabilities into Oracle E-Business Suite on OCI for federal financial management. Build NLP and document-processing solutions, REST API integrations, and end-to-end ML pipelines. Define AI/ML use cases, transition roadmaps, cognitive adoption metrics, and data-mining strategies. Participate in SAFe Agile delivery, write user stories, remove technical impediments, support DevSecOps integration, and produce transition and knowledge-transfer materials.

Lead and build a new platform engineering team responsible for cloud-agnostic deployments across AWS, other public clouds, private cloud, and on-premises environments. Own Kubernetes, infrastructure as code, GitOps, CI/CD, security compliance, FinOps, observability, incident response, and AI/ML infrastructure. Hire and develop engineers, establish operating standards, automate compliance evidence, and deploy AI agents for infrastructure operations.

Build and manage technical relationships with enterprise customers, helping them adopt JetBrains products, overcome technical and organizational barriers, and achieve business outcomes. Serve as a customer advocate, advise on software development tooling, collaborate with account executives and product teams, recommend product enhancements, support account expansion, and present product capabilities. The role requires customer-facing technical experience, AI/LLM knowledge, strong communication, software development team experience, and approximately 25% travel.

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.

Develop and productionize large-scale machine learning models, ranking algorithms, data pipelines, and real-time or batch systems for Airbnb search and recommendation. Collaborate with engineering, product, operations, and data science teams to prioritize opportunities, drive technical decisions, and measure business impact. Build scalable ML infrastructure supporting feature engineering, model serving, experimentation, interpretability, optimization, and concept drift detection.

Own cross-functional technical programs supporting Customer Success, coordinating Product, Engineering, NOC, and Operations. Validate customer-focused releases, configurations, and integrations; lead change management, enablement, communications, and training; triage customer-impacting technical issues; maintain PMO trackers, status reports, and risk logs; and advise leadership on priorities and customer needs.

Leads Experian Health’s Patient Financial Experience portfolio, setting multi-year product strategy, investment priorities, roadmap, and P&L accountability. Oversees patient scheduling, estimates, payments, financial clearance, statements, charity care, and collections products. Builds and develops a high-performing product organization, drives AI-enabled healthcare solutions, and represents the portfolio with customers and industry stakeholders. Requires deep healthcare revenue cycle expertise, regulatory fluency, executive communication skills, and experience managing multi-product portfolios and senior product managers.

Leads the architecture, development, deployment, and production support of secure, scalable SaaS software across a public safety platform. Champions AI-native development practices, including coding agents, automated testing, code review, and agent workflows. Mentors engineers, collaborates cross-functionally, improves reliability and performance, manages incident response, and implements secure development practices for sensitive criminal justice data.

Develop, test, optimize, document, and troubleshoot distributed robotics software for agricultural and construction equipment. The role uses C++ in Linux environments and involves deployment support, system debugging, API and architecture documentation, work estimation, and collaboration with embedded software, QA, mechatronics, computer vision, and machine learning teams. Preferred experience includes sensor integration, vision systems, GPU computing on edge devices, Python robotics development, and autonomous vehicles.

Designs challenging competitive data science and machine learning tasks, reproducible execution environments, reference solutions, automated tests, and evaluation criteria. Uses Python, statistical modeling, mathematical reasoning, and debugging to assess AI coding agents, identify data leakage and implementation flaws, validate solution correctness, and document methodologies. Requires a verifiable Kaggle Expert-level profile, strong practical programming skills, and the ability to independently test and explain technical solutions.

Develop and advance the organization’s Insider Threat Program by assessing capabilities, creating behavioral indicators, enhancing detection analytics, and investigating suspicious activity. The role analyzes security, fraud, and behavioral telemetry; configures monitoring technologies; reduces false positives; produces risk reporting and metrics; and partners with Cybersecurity, Fraud, HR, Legal, Risk, and Technology teams to improve insider-risk detection and response.

Lead research at the intersection of AI, cybersecurity, and computer systems. Define research problems, develop hypotheses, build prototypes, design rigorous experiments, evaluate AI and security methods, and translate findings into practical systems. Research areas may include AI agents, autonomous penetration testing, attack-path reasoning, cyber deception, AI safety, and security evaluation. Mentor junior researchers, collaborate with engineering and product teams, and communicate findings through reports, presentations, prototypes, or publications.

Independently lead complex technology and commercial transactions, including SaaS, cloud, licensing, vendor, partnership, and data-processing agreements. Advise cross-functional stakeholders on privacy, security, intellectual property, GDPR, AI, regulatory, and commercial matters. Improve contracting through templates, playbooks, CLM workflows, AI-enabled tools, analytics, and scalable legal operations. Analyze legislation and regulatory developments affecting cybersecurity, privacy, AI, and digital infrastructure.