Top Tech Jobs & Startup Jobs

Coordinate end-to-end interviews and scheduling across multiple searches, serve as the primary candidate contact, maintain accurate Ashby data, and support recruiting operations. The role partners with recruiters and hiring managers, manages onsite and virtual interview logistics, supports offers, identifies process bottlenecks, and implements AI-enabled workflow improvements. Success requires strong communication, organization, ownership, candidate focus, and comfort working in a fast-growing, ambiguous environment.

Supports the CEO by managing schedules, travel, meeting preparation, priorities, communications, stakeholder relationships, and follow-up. The role also involves researching business questions, coordinating strategic projects, building operational workflows, preparing briefings, and improving Office of the CEO processes. Success requires strong judgment, organization, resourcefulness, communication, confidentiality, and comfort handling ambiguous, fast-changing responsibilities in a high-growth AI company.

Research Engineers design, prototype, evaluate, and productionize agentic AI systems for complex enterprise workflows. They build architectures involving planning, tool use, retrieval, memory, orchestration, and execution; develop evaluation and observability frameworks; investigate reliability and failure modes; integrate agents with customer APIs and data platforms; and communicate capabilities, risks, and tradeoffs to technical teams and stakeholders.

Build data systems and pipelines that support reliable AI workflows in enterprise environments. Responsibilities include collecting, cleaning, labeling, transforming, and evaluating data; creating quality and drift frameworks; developing retrieval, synthetic data, annotation, and feedback-loop tools; analyzing customer datasets; and collaborating with researchers, engineers, and stakeholders to improve production AI performance.

Designs and runs post-training workflows to improve AI system behavior, reliability, and usefulness. Responsibilities include developing datasets, preference signals, evaluation suites, reward models, fine-tuning workflows, experimentation infrastructure, regression tests, and feedback loops. The role analyzes model outputs and production traces, investigates post-training techniques, adapts systems to customer domains, and collaborates with researchers, engineers, and stakeholders to deploy robust, measurable AI systems.

As an AI Strategist, you'll lead customer projects, embedding in workflows to identify AI opportunities, ensuring delivery quality, user adoption, and customer trust.

As an AI Platform Engineer, design and optimize the AI-native platform, develop scalable infrastructure, and collaborate with teams to solve complex AI deployment challenges.

Senior technical leader responsible for the success of complex enterprise AI deployments: setting architecture and evaluation strategy, ensuring production reliability, coaching and developing engineering leads, partnering with customer executives, debugging critical issues, and scaling pilots to enterprise deployments while using AI-native engineering practices.

Design, build, deploy, and operate production AI systems for customers. Implement end-to-end AI workflows (models, prompts, agents, retrieval, evaluators), write maintainable Python services, monitor and debug live systems, create evaluation and observability tooling, and work closely with stakeholders to iterate systems for reliability and business value.

Lead technical delivery and architecture of production AI systems for enterprise customers. Mentor engineers, integrate models/agents with customer data and APIs, operate and improve live systems, measure and debug failures, and drive pragmatic, reliable deployments that deliver business outcomes.