Top AI & Machine Learning Jobs

Leads enterprise architecture, design, governance, and adoption of AI, machine learning, generative AI, analytics, RPA, intelligent automation, and data platform solutions. Translates business strategy into scalable, secure, compliant architectures across Azure, AWS, SaaS, and Microsoft technologies. Partners with business, engineering, security, governance, and operations teams; establishes reusable standards and patterns; guides delivery from concept through production; leads architecture reviews, prototyping, performance improvements, compliance, and technical mentoring.

Leads the strategy, technical direction, and delivery of insurance data science across pricing, underwriting, cost modeling, demand modeling, and growth. Oversees predictive modeling standards, model lifecycle governance, engineering collaboration, regulatory alignment, and production deployment. Builds and develops a high-performing data science organization while partnering with actuarial, product, underwriting, pricing, and executive leadership to connect analytical work with profitability and business outcomes.

Lead the technical direction and implementation of Current’s machine learning platform, covering feature pipelines, training data, model training, serving, monitoring, reproducibility, and production-readiness standards. Establish scalable workflows, improve training-serving consistency, support data scientists, define model governance contracts, evaluate build-versus-buy tooling, and drive adoption across engineering, data science, risk, marketing, and finance.

Leads Fusion’s Technical Architecture practice while personally building production AI and LLM tooling for Professional Services. Responsibilities include managing and mentoring architects, setting architecture standards, supporting enterprise presales and regulated customers, modernizing delivery infrastructure, establishing AI security guardrails, evaluating emerging AI capabilities, and driving adoption through training and enablement. The role requires deep Salesforce or ServiceNow expertise, hands-on software development, enterprise integration knowledge, and executive-level customer engagement.

Leads Unanet’s internal data strategy and architecture for enterprise AI. Defines the shared data foundation, target architecture, governance standards, roadmap, and reusable approaches for AI assistants, agents, and workflows. Evaluates platforms and integration options, validates technical solutions, and coordinates IT, Security, Legal, Engineering, business owners, and system specialists. Advises senior leaders, establishes data quality and stewardship requirements, and helps determine when data is ready to support scalable AI initiatives.

Lead end-to-end LLM inference performance across serving systems, models, GPU hardware, and workloads. Define rigorous benchmarks, diagnose bottlenecks from scheduling through kernels and interconnects, optimize single- and multi-node deployments, and deliver production-ready runtimes and configurations. Collaborate with product and infrastructure teams, evaluate inference technologies, and implement runtime fixes. The role requires strong Python engineering, production experience with serving engines such as vLLM or SGLang, GPU profiling, and knowledge of modern inference optimization techniques.

Develop data science solutions for payments across Square and Cash App. Build AI-powered tools, ETL pipelines, dashboards, machine learning models, experiments, and cloud data systems. Partner with Product, Engineering, Finance, and Operations to improve payment success, cost efficiency, risk mitigation, and decision-making. Translate business needs into practical solutions, research questions independently, and operationalize data processes and infrastructure.

Build AI-powered features and products for clinical documentation and medical coding. Develop LLM-driven agentic systems, APIs, services, and production infrastructure across healthcare revenue cycle workflows. Create evaluation frameworks and experimentation loops using statistical and data-driven methods to ensure reliability, auditability, and alignment with coding guidelines and user feedback. Collaborate with product, design, machine learning, clinical, and revenue cycle experts to deliver high-impact SaaS solutions.

Build and operate Notion’s model layer by integrating frontier models, improving inference reliability, implementing failover and observability, and developing model-level capabilities. The role requires production experience with LLM APIs, evaluation-driven development, and strong judgment around latency, cost, reliability, and output quality. The engineer will also collaborate across AI teams to unblock adoption and ensure fixes are delivered.

Leads Utility’s AI practice by defining its roadmap, overseeing AI research and deployment, identifying client opportunities, mentoring AI teams, and establishing responsible AI practices. The role combines technical leadership, product strategy, client advisory, and operational execution across machine learning, generative AI, and intelligent automation initiatives.