Top AI & Machine Learning Jobs

Build and operate large-scale ML infrastructure, including feature pipelines, model training and evaluation systems, high-performance inference services, data management platforms, and model orchestration tools. The role partners with ML engineers to deploy models into production, improve system reliability and efficiency, and maintain secure, scalable, production-ready services using Python, Java, and related AI technologies.

Build and optimize a context-aware inference runtime for fine-tuned small language models across cloud, on-premises, NVIDIA, and AMD environments. Responsibilities include KV-cache management, speculative decoding, GPU kernel optimization, continuous batching, quantization, multi-GPU execution, adapter lifecycle management, observability, tenant isolation, reliability, and safe deployment. The role partners with research, model engineering, agent, knowledge systems, and platform teams.

Leads Coupa’s Customer Success and Support AI product portfolio, owning strategy, roadmap, business cases, agentic workflow design, delivery, governance, adoption, and measurable outcomes. Partners with AI Platform, Engineering, architecture, IT, and executive stakeholders to build and scale autonomous support and success workflows. Drives agent prototyping through production, performance monitoring, human-centric validation, change management, enablement, and organizational adoption while ensuring accuracy, compliance, trust, retention, and efficiency.

Conduct applied AI research and build foundation models across design, training, evaluation, validation, and production implementation. Collaborate with data scientists, software and machine learning engineers, and product managers to deliver scalable AI products. Research LLMs, optimization, fine-tuning, and agentic AI; publish findings through technical reports and conference submissions; and independently pursue impactful research agendas.

Manage AI and data science projects from planning through implementation, coordinating technical and non-technical teams, dependencies, Agile ceremonies, requirements, budgets, timelines, scope, and stakeholder expectations. Translate AI capabilities into actionable plans, navigate ambiguity, and drive accountability across multiple teams and priorities.

Lead the technical direction, architecture, and development of Compass, an LLM-powered agent platform. Design multi-agent systems, MCP and A2A integrations, orchestration, evaluation, and observability infrastructure. Drive cross-team technical projects, production support, engineering quality, mentoring, and adoption of AI-assisted development practices. Partner with Product, QA, Design, Operations, and Engineering stakeholders to deliver reliable agentic systems.

Lead the design, development, and adoption of production-grade AI systems within a strategic business domain. Define AI roadmaps, identify high-impact opportunities, make architecture decisions, establish evaluation and observability practices, measure business impact, mentor engineers, and drive AI transformation across cross-functional teams. The role focuses on LLMs, RAG, AI agents, scalable infrastructure, and reliable enterprise AI products.

Owns the enterprise AI engineering foundation, including shared AI platforms, semantic routing, model hosting, gateways, operations, agentic pipelines, and multi-agent orchestration. The role establishes architecture and standards, delivers secure production AI services, develops evaluation and governance capabilities, manages AI economics, prototypes emerging technologies, mentors senior engineers, and partners with security, risk, compliance, and data governance teams in a regulated financial and healthcare benefits environment.

Build and operate production-grade full-stack AI applications and custom agents using MCP, RAG, LLMs, and cloud-native technologies. Own features throughout design, implementation, testing, deployment, observability, and lifecycle support. Integrate AI services, APIs, databases, and Kubernetes platforms while ensuring reliability, security, scalability, and maintainability for critical manufacturing systems. Collaborate across engineering and business teams and use AI-assisted development practices to accelerate delivery.

Early-career data scientist supporting sector investment teams: deliver data pulls, analyses, trackers, dashboards, anomaly investigations, and reusable Python/SQL tooling. Use time-series methods, alternative data, and LLMs to accelerate workflows and build commercial judgment with investors.