Top Machine Learning Jobs in Hartford, CT

Lead the development of fraud prediction models using machine learning, collaborating across teams to build, scale, and monitor models in production.

Lead a team of ML engineers to develop fraud detection models, defining strategies, driving model evolution, and ensuring effective integration with cross-functional teams.

Lead design and delivery of ML solutions for utility operations, develop models and evaluation criteria, collaborate with engineers to productionize tools and web apps, support asset and grid analytics, and assist storm restoration and outage management through data-driven insights.

The Senior Software Engineer will design and optimize recommendation algorithms, maintain data pipelines, and improve personalized user experiences using machine learning techniques and backend engineering skills.

Lead the machine learning strategy, design scalable ML systems, drive MLOps, mentor engineers, and comply with AI governance.

As a Senior Staff Machine Learning Engineer at Affirm, you will lead complex ML system design and implementation, mentor engineers, and drive innovative ML strategies for financial services.

As a Principal Software Engineer, you'll architect and code backend services and ML models, integrate them with Salesforce, and mentor junior engineers.

Lead large Applied AI portfolios, ensuring value delivery through advanced AI systems while managing risks and governance across multiple teams. Collaborate with senior leaders to align AI initiatives with business outcomes and oversee effective execution of AI strategies.

Lead strategy and delivery for an AI-powered claims decision-support and auto-adjudication platform. Define vision and roadmap, manage a team of product managers, partner with business, engineering, and data science, and drive adoption, impact measurement, and executive-level communication across a complex, matrixed enterprise.

As a Staff Machine Learning Engineer, you will design AI products on Edge devices, optimize ML model performance, and work with large-scale datasets, collaborating across teams to deliver impactful solutions.

As an ML Ops Infrastructure Engineer, you will design CI/CD pipelines for ML, maintain deployment systems, and implement monitoring while collaborating with research teams to ensure model quality and performance.

As a Systems Architect, you'll define and drive the infrastructure architecture for AI/ML workloads, design multi-cloud strategies, manage compute orchestration and storage systems, and lead capacity planning, while also collaborating with engineering leadership.

The engineer will build and operate AI/ML infrastructure, managing services on AWS and bare metal, using tools like Kubernetes and Terraform.

Lead end-to-end research applying advanced ML/DL (transformers, GNNs, RL, ensembles) to generate alpha across digital asset markets, build production-grade pipelines, prevent leakage/overfitting, analyze microstructure and on-chain data, and mentor junior researchers.

The AI Sales Executive drives new business growth and expands client relationships by selling AI-driven solutions for engineering and manufacturing sectors, managing the full sales cycle with a consultative approach.

Own and implement end-to-end product features across the stack (React/TypeScript front end, Ruby on Rails backend, relational DBs, GCP infra). Design APIs and data models, ensure performance and reliability, integrate AI/LLM-powered features, collaborate with product/design/clinical teams, perform code reviews, and mentor junior engineers.

Own Oscilar's AML product suite (transaction monitoring, KYC/KYB, screening, case management, SAR filing, AI agents). Define detection rules, model calibration, data strategy, and defensible audit trails. Collaborate with engineers and compliance, use AI across discovery/design/analysis, deliver PRDs, validate models, and ship production features.

Lead the data and ML product vision: define roadmap, prototype models on noisy behavioral datasets, translate signals into user-facing features, partner with engineering to productionize ML, and generate insights to inform product roadmaps.

Lead and scale a global Deal Desk to structure complex SaaS transactions, own deal review and approval workflows, improve quote-to-cash across CRM/CPQ/CLM/billing/ERP, implement AI/ML for efficiency, provide analytics on discounting and deal quality, and build a high-performing global team while advising Sales and executives on strategic deals.

Build data foundations and end-to-end pipelines, research and implement quantitative pricing, market-making, and risk models for prediction markets, model cross-market dependencies and parlays, develop backtesting/simulation frameworks, monitor model performance and collaborate closely with traders to improve pricing and trading outcomes.

Lead high-impact strategy and BizOps workstreams including M&A diligence, investor relations, new-market expansion, and cross-market operations. Frame ambiguous, board-level problems, build analysis and recommendations, align stakeholders, and own execution end-to-end while leveraging AI and automation.

As a Forward Deployed Machine Learning Engineer, you'll build and deploy machine learning models, enhance system performance, and collaborate with Data Science and Engineering teams for effective solutions.

Support Midstream and Growth programs by developing and maintaining BI dashboards, translating business requirements into KPIs and data models, leading data validation and governance, providing ad-hoc analytics and scenario modeling, driving BI capability development, and partnering on strategic digital transformation and reporting roadmap initiatives.

Lead CX transformation engagements by driving client discovery, maturity assessments, journey analysis, and presales solutioning. Design Conversational AI/CCaaS-based solutions, craft proposals and demos, collaborate with sales and delivery, and influence C-level stakeholders to deliver measurable CX improvements and ROI.

This is a general talent-community application inviting candidates across many functions to submit resumes. Recruiting will review and retain applications for future openings; candidates should highlight experience, accomplishments, and interests for potential remote roles.