Top Machine Learning Jobs in Hartford, CT

Develop and deploy machine learning models and production software for multi-object tracking in autonomous trucks. Responsibilities include sensor fusion across camera, LiDAR, and radar; scalable PyTorch training workflows; tracking algorithm development; large-scale data analysis; C++ and Python engineering; safety and validation strategy; architecture decisions; and cross-functional collaboration. The role also provides technical leadership through design reviews, code reviews, mentoring, and engineering best practices.

Own end-to-end analytical lifecycle to develop and optimize business, risk, and growth strategies. Monitor portfolio and macro trends, build and interpret ML-driven models, lead cross-functional launches, mentor junior analysts, and build AI-assisted workflows to improve decisioning and measurement across products and geographies.

Develop agentic AI systems and machine learning methods for drug discovery, including molecular and tabular in-context learning, few-shot adaptation, foundation models, and biological data analysis. Lead research projects from task definition and dataset construction through model training, benchmarking, ablations, and scientific validation. Collaborate with chemists and biologists, build reproducible research software, scale training pipelines, and translate successful methods into drug discovery capabilities and scientific publications.

Lead technical strategy for a 50–150+ engineer organization, establish architecture and engineering standards, drive delivery, and partner with executives on technology investments. Embed production-grade generative AI, LLMs, agentic workflows, and ML automation into engineering. Own AI platforms, distributed systems, cloud-native backend architecture, modernization initiatives, microservices migrations, and engineering transformation while ensuring scalable, reliable outcomes.

Own product areas for an autonomous banking platform, translating backend capabilities (APIs, async workflows, ML) into user-facing features. Conduct customer discovery, define technical requirements and acceptance criteria, prioritize backlog, partner with engineering on scoping and de-risking, and validate outcomes using feedback loops and analytics.

Lead the Product and Design organization, define product vision and roadmaps, prioritize strategic investments, and align cross-functional teams to deliver safe, scalable robotic delivery experiences. Build and develop high-performing product and design teams, champion user-centered design, guide data-driven decisions, and translate complex robotics, hardware, software, and operational capabilities into customer value. Partner with executive leadership across Engineering, Operations, Safety, Policy, and Business Development to drive commercialization and growth.

Lead performance marketing data science initiatives focused on lead segmentation, bidding optimization, targeting, and marketing spend allocation. Develop and deploy production-ready machine learning systems, establish research and ML operations roadmaps, build monitoring frameworks, and identify high-impact opportunities. Partner with engineering, marketing, vendors, and executives while mentoring data scientists and machine learning engineers and promoting rigorous, maintainable, and reproducible practices.

Leads AI presales strategy, customer advisory, technical solution architecture, and team development. Translates business needs into AI use cases, prototypes, business cases, roadmaps, and repeatable offerings. Partners with sales, engineering, consultants, and technology vendors to support complex pursuits, executive briefings, demonstrations, proofs of concept, and AI business growth. Promotes responsible AI governance, tracks pipeline performance, develops thought leadership, and remains hands-on in high-impact customer engagements.

Drive new enterprise revenue and customer expansion by selling AI-powered SaaS solutions. Manage complex 6–12 month sales cycles, build executive relationships, develop value propositions, maintain pipeline accuracy, lead virtual account teams, and close high-value deals. Translate technical AI capabilities into measurable business outcomes, guide customer adoption, collaborate with marketing, product, and customer success teams, and exceed quarterly pipeline and revenue targets.

Architect and prototype scalable solutions for Twilio’s emerging technologies experimentation team. Lead AI, machine learning, and LLM-driven initiatives; guide technical direction; mentor engineers across the US and India; align prototypes with business goals; and ensure enterprise architecture, scalability, extensibility, and production readiness. The role requires extensive architecture or principal engineering experience, cloud expertise across AWS and Azure, full-stack development knowledge, database expertise, and strong technical leadership.

Annotate and label data accurately according to project guidelines, conduct quality checks, provide feedback on annotation tools and processes, collaborate with project teams, maintain efficiency, and identify issues for resolution. The role requires strong attention to detail, critical thinking, adaptability, communication skills, and comfort with repetitive computer-based work.

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

Staff-level ML and agent systems engineer responsible for production model lifecycles, multi-step agentic systems, evaluations, retrieval and context engineering, and model cost and latency optimization. The role owns technical direction for Code Understanding products, improves model and agent quality, establishes evaluation and monitoring standards, mentors engineers, collaborates with customers, and delivers reliable AI-powered developer experiences at enterprise scale.

Owns the strategy, roadmap, requirements, backlog, positioning, and lifecycle execution for proprietary software products. Partners with engineering, UX, QA, architecture, data operations, and project management teams in an Agile environment. Conducts market and competitive analysis, supports product launches, creates AI prototypes, develops client-facing content, and contributes to RFP responses. Requires strong technical, product leadership, communication, prioritization, and stakeholder-management skills.

Build rigorous ROI analyses, value models, and business cases that translate AI ranking performance into revenue, margin, and cost-to-serve outcomes. Present analyses to CFOs, procurement teams, and other economic buyers; support sales pursuits; create reusable value-selling frameworks; tailor models to customer economics; and collaborate with product, data science, and customer teams to validate benchmarks and deployment results.

Own the full Global System Integrator partner lifecycle, including sourcing, agreements, joint go-to-market motions, enablement, delivery planning, and performance optimization. Build relationships across major GSIs such as Accenture, Deloitte, Bain, BCG, Slalom, and IBM. Collaborate with sales, product, and engineering teams to generate partner-sourced and influenced pipeline and revenue. Develop the GSI strategy, playbook, and operating processes for Sequen’s growing AI business.

Own strategic enterprise customer relationships, driving adoption, retention, measurable value, and expansion. Lead success reviews and executive engagements, translate supply chain challenges into solution strategies, build prototypes in Lyric Studio, train customer teams, coordinate forward-deployed engineers, partner with Sales on growth opportunities, and relay customer needs to Product and Engineering. The role requires deep supply chain expertise, technical fluency in modeling, planning, or AI/ML, and strong executive relationship skills.

Leads innovation and enterprise solution strategy for healthcare payment integrity, developing scalable platforms using AI, automation, analytics, and data technologies. Engages senior clients, supports strategic pursuits, shapes transformation roadmaps, and builds partnerships. Oversees solution architecture, enterprise system development, prototyping, governance, performance metrics, and commercialization. The role also mentors cross-functional teams and translates complex healthcare payer challenges into differentiated, market-ready solutions.

Build and operate production machine learning systems for Airbnb Payments, including LLM agents, real-time fraud detection, anomaly detection, personalization, and checkout optimization. Design, train, deploy, monitor, and continuously improve batch and streaming AI applications at global scale. Partner with product, engineering, operations, and data science teams, establish model governance and testing standards, and mentor engineers.

Manage post-sales relationships with strategic customers, drive adoption and business outcomes, coordinate technology delivery projects, oversee customer issues and escalations, facilitate customer meetings and Quarterly Business Reviews, communicate customer needs internally, and track customer status and KPIs. The role supports financial institutions across North America and partners with technical, threat analysis, data science, product, and engineering teams.

Leads the software engineering organization responsible for scalable, reliable applications and systems integrating hardware, embedded software, edge computing, cloud infrastructure, and machine learning. Sets technical strategy, develops engineering leaders, drives product delivery, establishes engineering standards, and ensures operational excellence across national-scale deployments. Partners with Product, Deployment, Data Science, and executive leadership to align roadmaps, resolve risks, and build high-performing distributed teams.

Lead product strategy, roadmap execution, discovery, and delivery for Records Request Management and FOIA solutions. Define AI-enabled capabilities such as document discovery, automated redaction, classification, workflow optimization, and self-service. Partner with engineering, design, security, compliance, legal, sales, and customer teams to deliver secure, compliant products for federal and regulated public-sector customers. Translate FOIA and records-access requirements into product outcomes while supporting adoption, growth, and market expansion.

Drive go-to-market reporting, business intelligence, forecasting, dashboard development, and ad hoc analysis. The role standardizes forecasting processes, maintains analytical documentation, automates reporting and data pipelines using AI-assisted tools, and delivers actionable insights to Sales, Marketing, Engineering, Finance, and executive stakeholders.

Build and lead the organization-wide adoption of generative AI, agentic systems, and AI-assisted development. Design production automation agents, evaluate coding tools, modernize rules-based workflows with ML, and establish responsible AI guardrails. Partner with engineering, product, data, and clinical teams to identify high-impact opportunities, own the enablement roadmap, measure outcomes, and drive adoption through working software, documentation, and hands-on collaboration.

Supports responsible AI governance by developing frameworks, conducting fairness and risk assessments, monitoring regulatory requirements, maintaining compliance documentation, educating stakeholders, and improving AI oversight practices. Works across technical, legal, compliance, risk, and business teams to promote ethical, transparent, accountable, and trustworthy AI systems.