Jobs at Distyl AI

Scout exceptional talent for Distyl AI by independently identifying high-potential candidates, building talent maps, pursuing passive prospects, and managing a high-performing sourcing funnel. Partner with founders, executives, and recruiting teams on sourcing, process, and closing. Success is measured by the quality of hires and the accuracy of scouting judgment. The role is hybrid in San Francisco or New York and requires 20% travel.

Build, deploy, operate, and improve production AI systems for enterprise customers. Responsibilities include designing compound AI workflows using models, prompts, agents, tools, retrieval, evaluation, and integrations; developing Python services; monitoring reliability; debugging and iterating on live systems; creating evaluation and observability frameworks; and collaborating directly with French-speaking customers and subject matter experts.

Owns build, test, release, developer productivity, and agent-enabled engineering infrastructure across multi-cloud customer environments. Responsibilities include creating reproducible build systems, release pipelines, software supply-chain security controls, coding-agent workflows, developer self-service tools, observability, alerting, and engineering productivity metrics. Requires strong Python, Linux, container, Kubernetes, GitOps, infrastructure-as-code, cloud, and security expertise, plus leadership of complex projects and migrations.

Serve as the senior technical partner for strategic enterprise customers, shaping AI opportunities, architecture, feasibility, prototypes, and technical statements of work. Advise executive stakeholders, assess data, security, integration, and operational constraints, and maintain technical continuity through delivery. Translate customer patterns into reusable assets and product feedback while collaborating with go-to-market, product, forward-deployed engineering, and delivery teams.

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.

The Applied AI Researcher will explore new AI system architectures, develop prototypes, analyze data, and redefine human-AI collaboration for enterprise applications.

The Applied AI Researcher will design benchmark frameworks, conduct statistical evaluations, and leverage AI to redefine enterprise software usage and measure intelligent system performance.

The Applied AI Researcher will design complex AI systems, integrate diverse components, and innovate in the utilization of AI for enterprise workflows. They will also conduct research on system interactions and workflows while building prototypes and demonstrating AI capabilities.

Researcher will adapt foundation models for real-world applications, focusing on performance, alignment, and human-centric objectives to optimize AI systems for enterprises.

The Applied AI Researcher will design self-improving systems that evaluate and enhance performance through feedback and self-modeling. The role demands a creative approach to redefine software usage, focusing on continuous learning and improvement using advanced AI techniques.

Serve as a trusted advisor to CIOs/COOs at large enterprises, define AI-driven business and operating strategies, translate executive vision into multi-year programs, mobilize engineering and research teams, and grow enterprise relationships into large, multi-million dollar engagements while remaining accountable for measurable outcomes.