Top AI & Machine Learning Jobs

Embed with grocery customers to scope, integrate, and deploy production LLM- and agent-powered systems. Build and harden platform components (knowledge graph/ontology, retrieval, agent frameworks, serving, evals, and observability) so field learnings become reusable tooling. Balance customer-facing implementation with platform engineering to enable scalable, reliable AI in production.

Supports IT by researching, testing, documenting, and presenting AI use cases with Glean and related tools. Collects and validates data, assists with reports, dashboards, visualizations, and analytics using Excel, Power BI, SQL, Python, and Microsoft Fabric. Evaluates AI-generated results, documents workflows and recommendations, identifies process improvements, and communicates findings to business and technology stakeholders.

Leads the design, implementation, architecture, governance, and scaling of agentic automation solutions. Builds production agents using Microsoft Power Platform, Azure AI Foundry, browser automation, LLM reasoning, and Python. Establishes enterprise standards for agent reliability, security, recovery, hosting, monitoring, auditability, and human escalation while partnering with Security, Risk, and Compliance.

Leads ElectroKare’s scientific vision and machine learning research agenda for EKG waveform modeling and clinical prediction. Oversees ML engineers and researchers, guides sequence architecture decisions including Mamba, translates healthcare problems into ML solutions, partners on FDA validation, and represents the company’s science externally. Requires applied ML/DL research leadership, clinical or biomedical data experience, modern sequence modeling expertise, and advanced education or equivalent industry depth.

Lead the engineering team building Chariot’s AI claims platform, including agent systems for case origination and claims matching, human-in-the-loop review, evaluation infrastructure, and scalable AI performance. The role owns product decisions, internal tooling, cost efficiency, and production reliability while recruiting and developing full-stack and ML engineers. Experience with production software, end-to-end product ownership, early-stage startups, and modern LLM systems is valued.

Leads The Washington Post’s enterprise AI strategy, roadmap, governance, and transformation initiatives. Identifies high-value AI opportunities, develops investment cases, evaluates build-versus-buy decisions, establishes responsible AI and quality frameworks, and coordinates cross-functional adoption across editorial, product, technology, commercial, and corporate functions. The role also builds organizational AI capability, advises executives and the Board, measures business impact, and guides scalable, secure, ethical AI implementation.

Build backend architecture and infrastructure for LLM-driven agent systems, including orchestration, tool integration, context management, evaluation, observability, and production deployment. Develop services connecting AI models with insurance data and platform APIs, define reliable AI-native architectural patterns, and collaborate with product, design, and engineering teams to deliver underwriting workflows.

Optimize dataset quality and value through marketplace incentives, task targeting, bonuses, payouts, eligibility, and experimentation. Design treatment assignments and holdouts, evaluate incremental collection against objective functions, identify Goodhart effects, and translate findings into operational actions. Partner with software teams to ship incentive and payout changes into the collection product. Experience with marketplace experimentation, pricing, growth, real-money payouts, or product implementation is preferred.

Own AI quality for an agentic enterprise platform, including evaluation datasets, grading rubrics, regression testing, model benchmarking, cost optimization, observability, and feedback systems. Partner with engineering, product, design, sales, and client-facing teams to diagnose failures, improve prompts and models, define quality standards, and ship product changes. The role combines hands-on Python and SQL analysis with product management, LLM evaluation, RAG, document AI, and agent workflow expertise.

Lead product strategy and execution for conversation intelligence, voice, and dialing. Deliver AI-native experiences, ship AI/ML features, run discovery with customers, and manage a team of product managers to drive real-time coaching, deal acceleration, and leader-facing analytics across a B2B SaaS revenue platform.