Top Tech Jobs & Startup Jobs

Own the vision, strategy, and roadmap for an AI evaluations platform supporting offline and online evaluation, production monitoring, agentic testing, SME workflows, and quality dashboards. Partner with applied scientists, engineers, product teams, legal experts, and executives to define evaluation standards, calibrate LLM judges, assess build-versus-buy decisions, and help teams measure and improve AI performance.

Staff Engineer responsible for architecting and implementing secure, scalable full-stack cloud services processing petabytes of data. The role provides technical direction across frontend, backend, and data layers; resolves complex cross-organizational challenges; establishes engineering standards for performance, reliability, and security; aligns technical execution with product strategy; and mentors senior and lead engineers. The position requires extensive experience with cloud-native distributed systems, architecture, technical leadership, cross-team influence, and large-scale reliability.

Own the architecture and technical direction of a production agentic AI product. Lead agent orchestration, context pipelines, evaluation infrastructure, search, grounding, security, cloud platform decisions, and enterprise integrations. Establish quality standards, improve engineering practices, mentor senior engineers, guide hiring, and remain hands-on solving complex technical problems across React, services, and PostgreSQL.

Build and operate scalable AI and machine learning platforms, including automated training, deployment, monitoring, and lifecycle management. Implement CI/CD pipelines using containers, infrastructure as code, and orchestration technologies. Deploy and troubleshoot production models, track reliability and cost metrics, evaluate MLOps tools, and collaborate with engineering, data science, product, security, and data teams. The role also includes code and design reviews, mentoring junior engineers, and advancing AI platform practices.

Leads complex deal review and execution across pricing, licensing, contracts, approvals, exceptions, and operational handoffs. Partners with Sales, Legal, Finance, Billing, IT, and Revenue Operations to manage risk, ensure compliant and audit-ready execution, improve deal workflows, maintain documentation, and support process adoption. Uses operational data to identify gaps and recommend improvements while mentoring peers and managing service-level priorities.

Own and grow a portfolio of major law firm accounts, driving retention, renewals, expansions, and strategic relationships with senior legal stakeholders. Lead complex negotiations, coordinate cross-functional account teams, identify competitive risks, and advise sales leadership on segment strategy. Represent the company at industry events, build executive networks, mentor Account Executives, and contribute to strategic initiatives. The role requires enterprise SaaS sales expertise, legal technology knowledge, technical fluency in AI and databases, executive presence, and regular travel.

Build and own production AI agents end to end, from tool and prompt design through evaluation, frontend experiences, deployment, monitoring, and continuous improvement. Develop full-stack services and shared AI infrastructure, debug retrieval and model issues, measure production performance, contribute to architecture and documentation, review code, mentor engineers, and collaborate with product, design, founders, and customers.

Supports partner-focused sales through technical consulting, product demonstrations, proof-of-concept management, proposals, risk assessments, and workflow assistance. Serves as a Relativity subject matter expert, evangelist, and liaison across sales, product, and partnership teams. Develops partner enablement materials, communicates customer feedback, maintains product and security expertise, mentors junior team members, and supports account growth. The role requires extensive SaaS and sales engineering experience, Relativity certifications, strong communication skills, and 25% travel.

Build and own production AI agents end to end, including tools, prompts, retrieval, evaluations, backend services, and Word or web interfaces. Ship cloud-native features, maintain evaluation and observability systems, debug model and retrieval issues, and contribute to reliable infrastructure. Collaborate with founders, product, design, customers, and engineers while mentoring teammates and driving continuous production improvements.

Leads end-to-end development of AI agent capabilities, including architecture, orchestration, retrieval, evaluation, Word and web experiences, deployment, monitoring, and quality standards. Codes approximately half the time while owning technical roadmaps, release decisions, cross-team platform and security collaboration, customer-informed prioritization, hiring, and mentorship of a small engineering team.