Top Tech Jobs & Startup Jobs

Coordinates recruiting logistics, interview scheduling, candidate communication, and pipeline data while managing onboarding, benefits administration, HRIS records, employment documentation, and employee lifecycle processes. Partners with recruiters, hiring managers, and the People team to improve workflows, apply AI tools, support People programs, and handle confidential information. The role is hybrid in San Francisco with at least three office days per week.

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.

Lead and scale Distyl AI’s global IT organization, overseeing employee technology, identity and access management, endpoint and network security, SaaS governance, office technology, compliance, and corporate security operations. Build teams, strategy, processes, budgets, and controls supporting growth from 180 to 500+ employees. Personally execute high-priority initiatives involving automation, ITSM, access governance, business continuity, incident response, audits, customer security assessments, and international expansion.

Serve as Distyl AI’s first HR Business Partner, advising managers and leaders on performance, team structure, hiring plans, coaching, compensation, organizational design, and change management. Use engagement and attrition data to identify organizational issues, build scalable people frameworks, and support technical teams. The role also involves developing HR systems in a high-growth AI environment and working hybrid from the San Francisco or New York office at least three days weekly.

Build, deploy, and operate production AI systems for enterprise customers. Responsibilities include developing Python services, designing compound AI workflows with models, prompts, agents, tools, retrieval, and evaluations, monitoring system performance, debugging failures, and improving reliability. The role requires close collaboration with customers and subject matter experts, ownership of production outcomes, pragmatic delivery in ambiguous environments, fluent French communication, and 10–30% travel.

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.