Top Tech Jobs & Startup Jobs

Build and deploy production generative AI and agentic systems for enterprise clients. Responsibilities include identifying high-value problems, shaping architecture, developing multi-agent workflows, APIs, data pipelines, integrations, and distributed systems, and owning deployments through measurable impact. The role requires hands-on ownership of evaluation, observability, security, reliability, scalability, latency, and cost, along with direct collaboration and communication with senior client stakeholders.

Owns enterprise AI engagements from signed SOW through production deployment, adoption, and renewal. Leads client relationships, delivery scope, timelines, staffing, risks, executive communications, P&L, margin, change orders, and expansion opportunities. Manages AI Engagement Leads, Forward Deployed Engineers, and delivery teams while partnering with technical leaders on architecture and production readiness. Requires strong financial-services consulting or delivery experience, technical fluency in AI systems, executive presence, and the ability to work onsite with New York-area clients several days per week.

Lead end-to-end GenAI engagements for Fortune 500 clients: drive client relationships, select use cases, provide technical direction, manage delivery timelines, coordinate cross-functional teams, mitigate risks, and ensure business impact from POC to full-scale implementation.

Own end-to-end delivery of software-engineering data programs, including scope, timelines, quality, throughput, cost, contributor performance, and customer reporting. Coordinate hundreds of distributed engineers, optimize annotation and evaluation workflows, analyze quality data, build automation and reusable playbooks, and serve as the primary client contact for AI research organizations. The role requires technical fluency sufficient to inspect code, tests, workflows, and evaluation results while leading large-scale operational execution.

Own the end-to-end development and delivery of reinforcement learning environments for frontier AI labs and enterprise clients. Lead environment design, task generation, reward and verifier development, data quality, evaluation, and post-training experiments. Build and manage cross-functional engineering, research, domain expert, QA, and data operations teams. Establish technical processes, schemas, APIs, validation scripts, and review systems while managing customer relationships, solutioning, delivery, and project growth.

Owns end-to-end development of datasets, reinforcement learning environments, and evaluations for coding agents and software engineering models. Leads engineers, researchers, QA professionals, and data creators; establishes quality processes, pipelines, validation, and review workflows. Collaborates directly with frontier AI labs, fine-tunes models, builds benchmarks, develops automation and internal tools, and supports customer-facing enablement. The role combines hands-on technical leadership, engineering management, operational execution, research, and industry thought leadership.

The Staff Research Engineer - Code will lead the creation of datasets and reinforcement learning environments for coding agents, collaborating with AI labs to influence model improvements and operational excellence, while managing a technical team.

Lead end-to-end delivery of software-engineering data programs for frontier AI labs: design and run data pipelines, coordinate 100–1,000+ contributors, ensure annotation and dataset quality, manage client relationships, diagnose bottlenecks, and codify scalable operational playbooks.

Support the CEO as an external force multiplier: run capital formation processes, craft investor/partner decks and memos, drive strategic partnerships with frontier labs and enterprises, build executive relationship systems, and lead high-impact confidential projects requiring judgment, discretion, and flawless execution.

Set technical direction across multiple services and teams; lead cross-cutting initiatives like migrations and modernization; own reliability and performance at scale; raise engineering standards through design docs and reviews; mentor senior engineers and partner with leadership on technical roadmaps.