Top Senior Machine Learning Jobs in Hartford, CT

Lead end-to-end healthcare analytics projects, managing scope, planning, execution, client relationships, cross-functional collaboration, risks, and delivery. Provide thought leadership in healthcare analytics and identify business opportunities within client accounts. Required capabilities include quantitative analysis, project management, communication, presentation, SQL/Python/R proficiency, and strategic use of AI tools. Preferred experience includes consulting, cloud analytics platforms, healthcare data regulations, predictive modeling, machine learning, and data visualization.

Leads architecture and development of production-grade mapping and localization ML systems for autonomous vehicles. Designs neural models for detection, segmentation, tracking, depth estimation, 3D reconstruction, and semantic mapping; owns data strategy, evaluation, deployment, and fleet validation. Mentors engineers, drives technical roadmaps, collaborates cross-functionally, and advances vectorized HD mapping, foundation models, and real-time optimization.

Own end-to-end design for complex financial product experiences from exploration through launch and iteration. Translate financial workflows into intuitive customer experiences, set high standards for interaction and visual design, partner with product and engineering, use research and data to guide decisions, contribute to design systems, present work clearly, and mentor teammates. Apply AI-enabled tools thoughtfully while maintaining strong human-centered judgment.

Design and implement scalable machine learning pipelines for insurance use cases, including classical ML and LLM workloads. Lead production deployments, build reusable ML infrastructure and CI/CD pipelines, and establish observability, testing, model monitoring, drift detection, logging, and rollback standards. Mentor junior engineers, collaborate cross-functionally, manage cloud-based ML systems, and communicate technical findings to nontechnical stakeholders while maintaining insurance data compliance.

Leads an engineering team building and scaling customer-facing integrations for agentic AI products. Responsibilities include setting technical direction, balancing customer delivery with reusable tooling and platform investments, ensuring reliability and observability, improving developer experience, collaborating cross-functionally, recruiting, and coaching engineers. The role requires customer empathy, strong engineering leadership, technical problem-solving, and experience with production systems or integrations.

Own and scale the customer entry-to-purchase lifecycle: design acquisition journeys, build and optimize welcome/abandon/lead conversion automations, implement personalization and gamification, partner cross-functionally, steward list health and deliverability, and run A/B and multivariate testing to improve conversion and engagement metrics.

Lead research and experimentation to measure ad campaign performance and effectiveness. Design evaluation frameworks, build measurement solutions, develop models for lift, conversion, and attribution, run experiments and analyses, partner with product/engineering/design/sales, present insights, mentor teammates, and foster technical community and culture.

Build and operate scalable, highly available ML/LLM serving and compute infrastructure. Own long-term platform vision, deliver zero-to-one projects, partner cross-functionally, optimize performance and capacity, and support production ML models at scale.

Lead design and execution of scalable experimentation frameworks for Netflix Ads. Develop auction-based, biddable media models, solve cold-start measurement problems globally, apply causal inference and ML, partner with product and engineering, and translate analytic results into strategic recommendations to optimize marketplace dynamics and member experience.

Design, productionize, and deploy machine learning forecasting models for ad inventory and campaign performance in low-latency, real-time advertising environments. Build scalable simulation, budgeting, and pacing systems; collaborate with cross-functional teams to deploy models at scale and optimize ad monetization.

Lead design, execution, and optimization of scaled lifecycle campaigns across email and in-product channels. Use marketing automation, behavioral data, and ML techniques to personalize journeys, drive activation, revenue, retention, and cross-sell. Analyze performance, generate insights, and collaborate with Growth, Product, Engineering, Data Science, Analytics, Sales, and Marketing stakeholders.

Lead design, execution, and interpretation of A/B tests and quasi-experiments; apply causal inference methods; partner with product, engineering, and marketing; mentor data scientists; build experimentation frameworks; extract and analyze data using SQL and Python; communicate results to stakeholders.

Lead integration of state-of-the-art AI/ML into Netflixs content platform and publishing tools. Build roadmaps, incubate ML systems from idea to production, partner with researchers and engineers, automate promotional asset creation, optimize distribution and localization, and measure impact with data-driven evaluation.

The Cloud Inference Engineer is responsible for building and maintaining a scalable LLM inference platform, focusing on distributed systems and machine learning technologies while collaborating across teams to optimize performance.

Lead product strategy and execution for a B2B SaaS marketing analytics platform. Translate research into requirements, own the backlog, partner with engineering and GTM, define success metrics, and ship API- and data-heavy features for technical users.

The Senior Product Manager will set product direction, own the development lifecycle, collaborate with teams, and ensure high-quality outputs. This role requires strong opinions on product quality and customer insights.

Lead the Decision Science function, designing ML models, overseeing experimentation frameworks, and ensuring model adoption across the company, driving operational and commercial improvements.

Lead and build a team that drafts and submits state-specific rating rules, underwriting guidelines, and forms; manage filing execution via SERFF, handle objection responses, coordinate cross-functional stakeholders, establish operating rhythms, improve documentation and controls, and coach staff to scale filing expertise and regulatory execution.

Lead net-new enterprise sales into Fortune 500 and large mid-market accounts building AI/ML systems. Prospect and create outbound pipeline, run long consultative sales cycles, orchestrate pilots and phased rollouts, influence technical and executive stakeholders, and partner with solution architects to close complex, high-value deals.

Author and verify rigorous multiple-choice computer science assessment items across many CS domains to create gold-standard benchmarks for AI research. Produce correct answers, nine plausible distractors, solution explanations, difficulty labels, and authoritative references. Review and edit questions for technical accuracy, clarity, and reproducibility. Contribute asynchronously as an independent contractor, ~10+ hours/week, fully remote.

Own end-to-end client relationships for a book of B2B SaaS customers: drive adoption, engagement, retention, and expansion of AI-powered contract drafting tools. Lead onboarding, training, large-scale rollouts, and translate customer feedback into product improvements while designing scalable CS processes and tracking health metrics to demonstrate value.

Serve as primary client contact for a portfolio of loyalty-program clients. Scope and manage end-to-end actuarial analyses rooted in predictive modeling and machine learning, validate model outputs, translate results into clear recommendations and presentations, and partner with analysts and data scientists to deliver high-quality, client-facing work in a fully remote consulting environment.

Lead and deliver complex, cross-team AI product programs, drive agent-first SDLC adoption, ensure integration and operational readiness of AI workflows, define metrics, communicate risks and status, and standardize TPM best practices across Product and Engineering.

Lead sales operations for seller acquisition at a marketplace: design pipeline and lead flows, remove onboarding friction, build SOT dashboards and forecasts, track listing health and KPIs, design incentive and quota programs, own CRM and sales enablement, and implement AI-driven automation and predictive analytics to scale seller and listing growth.

Partner with a retained search firm to place software engineers and quant developers at top hedge funds. Provide candidate flow from your network; firm supplies clients, process, and fees. Remote, commission-only role with placement and pipeline incentives. Apply with placement examples, outreach metrics, and a candidate submission.