Top AI & Machine Learning Jobs

Leads a data science team delivering personalized digital experiences through machine learning, generative AI, recommendation systems, predictive modeling, and responsible AI practices. Owns the AI lifecycle from experimentation and model development through deployment, monitoring, governance, and optimization. Partners with product, engineering, design, executives, and business leaders to identify opportunities, shape AI strategy, and integrate scalable solutions into customer-facing platforms.

Lead a multidisciplinary data science and engineering team to build automated, production ML and agentic systems for content ingestion, launch, and distribution. Set technical vision and standards, drive cross-functional roadmaps, mentor and grow talent, and ensure measurable business impact through rigorous evaluation and execution.

Partner with Product, Engineering, and Go-To-Market teams to define success metrics, design experiments, evaluate product launches, analyze large-scale ad-tech data, and guide roadmap decisions. The role requires translating complex data science findings into actionable recommendations for technical and non-technical stakeholders while applying rigorous statistical, modeling, and experimentation practices.

Design and implement solutions for Digital Lab Operations, ensuring alignment with architecture strategy and governance standards, and collaborate with various teams to streamline processes and optimize IT solutions.

Build and productionize end-to-end machine learning models, automated bidding and allocation algorithms, and data pipelines to optimize performance marketing spend. Manage ad platform integrations and bidding automation, automate creative/design workflows, solve loosely defined optimization problems, and collaborate cross-functionally with marketing, product, and leadership.

Lead the product backlog and roadmap for an AI-powered Next Best Action platform. Coordinate data pipelines, model feedback, and channel integrations (Adobe AEP, email/SMS/direct mail). Write implementation-ready user stories, manage rules/eligibility governance, align stakeholders on model and campaign outcomes, and ensure data quality for ML training and scoring.

Establishes and operates enterprise Data and AI governance frameworks across the full lifecycle. Develops responsible AI, privacy, security, risk, compliance, and model assurance controls; oversees data and model governance, vendor requirements, monitoring, and audit readiness. Partners with legal, cybersecurity, compliance, architecture, risk, and business teams to embed governance into delivery processes. Creates standards, tooling, training, metrics, and reporting while monitoring evolving regulations and technology trends.

Lead forward-deployed delivery of production data science, AI/ML, workflow automation, and enterprise integration solutions for defense, intelligence, aerospace, and government customers. Design data pipelines, predictive models, LLM workflows, APIs, and orchestrated processes using platforms such as MuleSoft and Regrello. Work onsite with mission owners, support secure DoD environments and ATOs, mentor junior engineers, translate mission needs into technical roadmaps, and travel frequently to classified customer facilities.

Develop generative and predictive machine learning models for therapeutic antibody sequence, structure, and property design. Build multi-objective optimization and agentic LLM methods for lab-in-the-loop protein engineering, deploy models with engineering teams, and support drug development programs. Collaborate with drug developers, wet-lab scientists, automation specialists, and data scientists to translate ML methods into validated antibody candidates.

Leads enterprise architecture, technical strategy, and evolution of GEICO’s Generative AI, virtual agent, and agentic workflow platforms. Designs scalable systems for retrieval, knowledge management, prompt and context management, orchestration, semantic search, and action execution. Evaluates technologies, establishes GenAI application standards, guides production deployments, influences business strategy, mentors senior engineers, and serves as a final technical authority across multiple teams and business domains.