Top Machine Learning Jobs in Hartford, CT

Lead long-term technical strategy and execution across multiple teams, architecting and evolving foundational frontend/backend systems and platforms at massive scale. Drive AI/ML adoption, solve ambiguous, high-impact problems, remain hands-on in critical system design and implementation, and align senior leaders on roadmaps and engineering standards.

The Staff Machine Learning Engineer will lead ML initiatives, develop ML systems, collaborate with cross-functional teams, and mentor junior engineers to optimize healthcare intake workflows using advanced machine learning techniques.

Embed in Research to drive end-to-end delivery of STT/TTS model versions and features. Map dependencies and risks, run cross-functional programs, establish lightweight processes, and communicate status across Research, Product, DataOps, and Engineering to ensure timely launches.

Lead the design and implementation of critical Safety AI initiatives, focusing on real-time perception systems for edge devices and optimizing models for production deployment in safety-critical environments.

The role involves deploying and supporting CrowdStrike's AI capabilities in cybersecurity, optimizing threat detection, and mentoring teams on AI solutions.

Manage a team of ML engineers to build and optimize Affirm's economic decisioning engine using advanced ML techniques. Collaborate across teams, provide technical leadership, and mentor engineers while driving a cohesive long-term ML strategy.

Lead finance transformation engagements using Oracle Cloud ERP and EPM. Design and implement Oracle Financials and Hyperion solutions, integrate RPA/ML/analytics, ensure compliance, manage stakeholder relationships, coach teams, and drive strategic outcomes on large, cross-border projects.

The Staff Product Manager, Conversion ML will shape product strategy for Liftoff's machine learning system, Cortex, develop new ideas, conduct experiments, and adapt roadmap based on data.

Lead the In-Vehicle Experience team at Samsara, focusing on transforming in-cab cameras into intelligent companions. Collaborate with engineering, prioritize features, and ensure client interactions highlight trust and connection. Conduct customer research and integrate AI and ML into product features.

Lead Machine Learning Operations initiatives by developing retail-specific models and a user-friendly Model Workbench for marketers. Ensure product development aligns with customer needs.

As a Senior Data Scientist, you will lead high-complexity projects, develop ML and NLP solutions, and collaborate across teams to drive business impact through statistical modeling and data analysis.

Lead the development of machine learning systems for Cedar Pay's Personalization Engine, manage ML engineers, and ensure ML model effectiveness.

The Enterprise Account Executive will manage the complete sales cycle, build relationships with senior leaders, and drive platform adoption within data-intensive organizations.

The AI Engineer Manager leads the Data and Analytics Engineering team in developing AI solutions, manages client accounts, analyzes data, and fosters team leadership and relationships with clients.

The Analytics Lead will leverage data analytics to optimize credit strategies, monitor portfolio performance, and collaborate on credit policy with stakeholders across technical and non-technical teams.

Lead Pluralsight's curriculum strategy to identify and shape future skills; translate market and technology trends into a curriculum roadmap; align with product, GTM, and content production; measure portfolio performance with data; lead and develop curriculum directors and managers; define scalable frameworks and represent the company externally.

The role leads the design and delivery of AI-powered technology to enhance productivity in claims operations, managing cross-functional teams to achieve strategic objectives.

The Director of Data Sciences leads a team in strategic initiatives, drives machine learning and AI solutions, and manages resources to enhance data-driven decision making across the business unit.

Tenure-track Assistant Professor expected to establish an independent, funded research program in quantum physics or AI-applied physics, mentor graduate and undergraduate students, teach one in-person course per semester, run a small seminar, advise students, and participate in departmental and university governance. Commitment to inclusive teaching and modern pedagogy required.

Lead Applied AI strategy and execution across Employee Benefits and Operations, overseeing ML, generative and agentic AI, RAG, and predictive analytics lifecycles. Drive governance, model risk management, evaluation, deployment, cross-LOB prioritization, talent architecture, and regulatory readiness to improve enrollment, billing, service, and operational efficiency.

The Engineering Manager, Data Science will lead a team to drive machine learning initiatives in AdTech, focusing on optimization and modeling strategies to enhance client success and operational efficiency.

The AVP Applied AI leads data science and AI capabilities at The Hartford, managing complex AI portfolios and ensuring delivery of business value through scalable models and AI-driven systems. Responsibilities include overseeing team performance, technical direction, AI governance, and alignment with business outcomes, along with collaboration across departments and continuous improvement initiatives.

Lead product analytics to inform strategy and optimize user experience: translate business questions into analysis, build reliable analytics infrastructure, run A/B tests and ML/statistical analyses, track funnels and cohorts, support 0-to-1 product launches, and communicate actionable insights to cross-functional teams.

Develop scalable machine learning services for data enrichment, managing the ML lifecycle from model training to deployment, and ensuring performance and reliability standards.

Build and operate ML training and serving infrastructure by designing, developing, and launching backend systems. Collaborate across teams to decompose projects, support operations and on-call, create monitoring and metrics, perform code reviews, and contribute to developer velocity and platform reliability.