Top Tech Jobs & Startup Jobs

Staff qualified contractors onto client projects by sourcing, screening, matching, onboarding, and tracking them through their first billed hour. The role manages high-volume contingent staffing, collaborates with delivery and operations teams, maintains ATS and staffing data, redeploys contractors, and reports on fill rates, time-to-staff, time-to-billing, and utilization.

The Enterprise Solutions Manager drives operational excellence and client success through strategic analysis, process optimization, and project management, collaborating with various teams to deliver AI solutions.

Drives enterprise client success and operational excellence by scoping requirements, designing scalable processes, coordinating cross-functional delivery, analyzing performance, optimizing quality, and supporting analytics initiatives. Acts as a bridge between enterprise clients, a global talent network, and internal product, engineering, and operations teams. The role also includes mentoring operational staff, managing stakeholder relationships, and helping build new marketplace capabilities.

Lead the technical strategy and multi-year roadmap for Data Labs, overseeing AI benchmarking systems, data pipelines, and reinforcement-learning environments. Manage engineering managers, forward-deployed engineers, and senior individual contributors while partnering with research, product, commercial, and executive teams. Guide platform investment, evaluation methodology, market positioning, client engagement, organizational growth, and architectural decisions supporting AI training and deployment.

Own revenue growth across foundation AI labs, technology companies, and enterprises. The role leads account expansion, new-logo sales, multi-million-dollar negotiations, forecasting, and executive relationships while shaping product packaging, pricing, and new revenue lines. It also partners with Product, Engineering, Delivery, and Operations to commercialize AI data, evaluation, staffing, and agentic offerings, build repeatable sales motions, lead a growing commercial team, and represent the company in the frontier AI community.

Build and deploy end-to-end statistical and machine learning solutions for clients and internal delivery teams. Responsibilities include scoping ambiguous problems, analyzing operational challenges, prototyping AI workflows, translating messy data and real-world constraints into scalable solutions, iterating with stakeholders, and developing reusable tooling and infrastructure. The role combines hands-on data science with technical consulting and requires communication with technical and non-technical stakeholders.

Lead end-to-end data science and machine learning projects, from scoping ambiguous client problems through production deployment. Build and evaluate statistical and ML models, analyze large datasets, run experiments, collaborate with engineers, create reusable tooling, communicate findings to diverse stakeholders, and mentor data scientists. The role combines hands-on data science with technical consulting and requires occasional customer on-call support.

Generalist People Experience Manager responsible for creating HR policies, competency frameworks, communications, and manager resources; managing People team programs; handling US employee relations cases; and improving People Operations processes. The role coordinates cross-functional stakeholders, supports HRIS and compliance projects, analyzes workforce data, and operates effectively in a fast-growing, distributed organization.

Build and deploy AI-powered solutions for clients by scoping ambiguous problems, designing LLM workflows, integrating data and retrieval systems, and creating scalable production tooling. Collaborate with technical and non-technical stakeholders, iterate based on operator feedback, optimize workflows, and support customer deployments. The role requires strong Python and software or ML engineering skills, production deployment experience, communication abilities, customer on-call availability, travel as needed, and hybrid office attendance.

Build and maintain evergreen technical talent pipelines for data engineering, machine learning, and full-stack roles. Source and engage candidates, coordinate with sales, delivery, engineering, and hiring leaders, define technical assessments, manage full-cycle recruiting, optimize sourcing and conversion metrics, and maintain candidate readiness for rapid deployment. This is a 3–6 month contract engagement at approximately 40 hours per week.