Top AI & Machine Learning Jobs

Lead the AI Quality engineering team while remaining hands-on. Build evaluation infrastructure, CI/CD pipelines, scorers, datasets, annotation workflows, observability tooling, and diagnostic systems for AI agents. Run experiments across prompts, models, caching, routing, and agent harnesses to improve quality, latency, and cost. Partner with AI Core engineering to validate production changes and manage a growing technical roadmap.

Build evaluation infrastructure, observability tools, diagnostic systems, datasets, annotation workflows, and experiments for production AI agents. Lead a hands-on AI Quality engineering team, improve agent reliability, latency, and cost, and partner with AI Core engineering. The role requires managing priorities while contributing directly to Ruby on Rails and Python codebases.

Lead the AI Quality engineering team while building evaluation infrastructure, observability tooling, datasets, annotation workflows, and diagnostic systems for production AI agents. Design CI/CD evaluation pipelines, run experiments across prompts and models, diagnose agent failures, improve latency and cost, and partner with AI Core engineering. This is a hands-on player-coach role requiring production software experience and expertise in evaluating complex, tool-using LLM systems.

Build and own evaluation infrastructure for AI agents, including CI/CD pipelines, scorers, datasets, annotation workflows, observability tools, and diagnostic systems. Run experiments across prompts, models, and agent harnesses to improve quality, latency, and cost. Lead the AI Quality engineering team while remaining hands-on, partnering closely with AI Core engineering to validate production improvements.

Lead the AI Quality engineering team while remaining hands-on building evaluation infrastructure, CI/CD pipelines, scorers, datasets, observability tooling, and diagnostic systems for production AI agents. Diagnose agent failures, develop annotation and simulation workflows, evaluate models and prompts, and optimize latency and cost through model routing and caching. Partner with AI Core engineering to improve reliability and manage the team’s technical direction and priorities.

Lead the AI Quality engineering team while building evaluation infrastructure, observability tooling, diagnostic systems, datasets, annotation workflows, and CI/CD pipelines for production AI agents. Run experiments across prompts, models, caching, and routing to improve quality, latency, and cost. Partner with AI Core engineering and remain hands-on in Ruby on Rails and Python development.

Lead the AI Quality engineering team while remaining hands-on building evaluation infrastructure, observability tooling, diagnostic systems, datasets, annotation workflows, and CI/CD pipelines for AI agents. Design experiments across prompts, models, and agent harnesses; diagnose failures; improve quality, latency, and cost; and partner with AI Core engineering to validate production changes.

Lead the AI Quality engineering team while building evaluation infrastructure, CI/CD pipelines, scorers, datasets, observability tools, and diagnostic systems for production AI agents. Run experiments across prompts, models, routing, caching, and agent orchestration to improve quality, latency, and cost. Manage team priorities, remain hands-on in development, and partner with AI Core engineering to validate product improvements.

Lead the AI Quality engineering team while building evaluation infrastructure, observability tools, diagnostic systems, datasets, annotation workflows, and experimentation frameworks for production AI agents. Diagnose agent failures, evaluate models and prompts, improve quality, latency, and cost, and partner with AI Core engineering. This is a hands-on player-coach role involving technical direction, team management, and production software development.