Top AI & Machine Learning Jobs

Leads strategic technical engagements for an AI inference platform from architecture evaluation through benchmarking, model enablement, deployment, and production rollout. Builds and manages a high-performing solutions engineering team, guides customer evaluations and deployments, translates customer requirements into product priorities, and develops scalable technical go-to-market assets. Requires deep expertise in AI inference, accelerator architecture, distributed systems, serving frameworks, performance optimization, and customer-facing technical leadership.

Own the architecture and technical direction of agentic AI platforms and intelligent applications across web, mobile, backend, conversational, and voice interfaces. Define agent orchestration, tool integration, retrieval, memory, evaluation, observability, security, and governance standards. Lead client-facing solution architecture, establish reusable frameworks and reference architectures, and guide teams on model selection and reliability, cost, latency, and security tradeoffs. Prototype complex solutions while shaping organization-wide technical direction.

Build and operate Retool’s production agent platform across product, infrastructure, and evaluation layers. Design agent prompting, context construction, retrieval, routing, tool use, sandboxing, and evaluation systems. Diagnose nondeterministic failures, improve reliability and safety, and partner on workload provisioning and deployment across cloud and self-hosted environments. Mentor engineers and establish standards for dependable agentic products.

Supports scalable ETL/ELT pipelines, cloud data platforms, database integration, machine learning model development and deployment, AI-enabled workflows, and analytics dashboards. Uses Python, data science libraries, AWS, GCP, Azure, SQL and NoSQL databases, Looker, Tableau, and automation tools. Collaborates with data engineers, analysts, data scientists, and marketing teams to deliver data-driven solutions. The role is hybrid in Dallas, requiring two in-office days weekly.

Build and ship AI-powered internal systems across investing, talent, founder services, finance, and operations. Projects include talent intelligence, sourcing and research tools, diligence workflows, firm knowledge search, operations automation, and reusable portfolio-company AI tools. The role requires end-to-end ownership, from identifying high-leverage problems and selecting the technical stack to building usable products, working with messy business data, and supporting portfolio founders with AI strategy and implementation.

Own the architecture and technical direction of a production perception system for home security hardware. Build robust computer vision and machine learning pipelines for multi-camera tracking, sensor fusion, inference, and edge deployment. Improve accuracy, latency, reliability, observability, and graceful failure handling across challenging environmental conditions. Lead evaluation infrastructure, field-data feedback loops, system interfaces, technical reviews, and cross-functional collaboration with hardware, cloud, and product teams.

Develop and deploy models to understand technical drawings and specifications, perform multimodal reasoning across diagrams, text, and structured data, translate research into production within ~1 year, present prior research, and collaborate closely with founders to deliver customer-impacting applied research.

Build production-grade AI and agentic systems for healthcare workflows. Responsibilities include designing data pipelines, evaluation harnesses, model deployment and monitoring systems, and scalable LLM- and vision-based applications. The role spans backend and full-stack development, AI agent orchestration, experimentation, training pipelines, and frontend integration while collaborating with ML researchers and engineering teams.

Leads Elastic’s agentic engineering platform for professional services. Designs and deploys production LLM systems using orchestration, tool integration, RAG, structured outputs, evaluation frameworks, tracing, monitoring, and human approval workflows. Owns architecture, technical standards, roadmap, quality, security, and lifecycle optimization while collaborating with services teams. The role emphasizes production reliability, sensitive-data protection, and client-ready outputs, with valuable experience in SIEM, observability, data migration, and Elasticsearch.

Build and productionize computer vision, 3D perception, and LLM-powered systems using proprietary LiDAR and imagery datasets. Own AI models throughout data preparation, training, evaluation, deployment, and monitoring. Develop agentic workflows, MCP integrations, and AI copilots while creating metrics tied to customer outcomes. Collaborate across hardware, software, and customer teams to rapidly prototype and deliver reliable infrastructure intelligence products.