Jobs at Turing

Investigate fraud and insider-threat cases from initial signal through resolution. Gather and analyze logs, transactions, behavioral signals, and other evidence; triage alerts; reconstruct events; document findings objectively; maintain evidence chain of custody; and collaborate with Detection & Response, Legal, and HR. The role also contributes to investigation playbooks, detection improvements, and fraud and insider-risk prevention.

Investigate fraud and insider-threat cases from initial signals through resolution. Gather and analyze logs, transactions, behavioral data, and other evidence; triage alerts; reconstruct events; document findings objectively; preserve evidence; and collaborate with Detection & Response, Legal, and HR. The role also contributes to investigation playbooks, detection improvements, and prevention efforts while handling confidential information with discretion.

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.

Build and deploy production generative AI systems for enterprise clients, from problem discovery and architecture through implementation, deployment, and measurable business impact. Responsibilities include multi-agent workflows, RAG, tool use, data pipelines, integrations, distributed systems, security, reliability, observability, scalability, latency, and cost optimization. The role requires direct client collaboration, stakeholder communication, technical leadership, and completion of an intensive eight-week applied AI training placement.

Lead corporate security engineering across endpoints, identity, SaaS applications, and corporate networks. Design zero-trust access architecture, manage EDR and MDM controls, automate provisioning and access reviews, secure SaaS configurations, and develop high-signal detections. Lead responses to phishing, account takeover, and endpoint incidents while setting technical standards, driving strategy, and mentoring security and IT engineers.

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.

Lead product marketing for Turing’s Frontier AI business by developing positioning, messaging, launch plans, technical content, customer research, competitive intelligence, and sales enablement. Partner with AI researchers, Sales, and Customer Success to translate complex AI capabilities into compelling value propositions, support product adoption, and inform roadmap decisions through customer and market insights.

Own the vision, roadmap, prioritization, and delivery of back-office operations and controls platforms supporting fund accounting and global finance. Partner with controllers, finance, operations, engineering, and technology teams to modernize workflows, reporting, automation, and governance. Translate fund control requirements into product specifications, user stories, and acceptance criteria while managing dependencies, risks, milestones, and stakeholder communication. Identify practical AI opportunities to improve operational efficiency.

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 Client Director will lead large-scale LLM training programs, managing global teams, ensuring data quality, and overseeing client relationships. Responsibilities include optimizing workflows, developing automation tools, and enhancing model performance through high-quality datasets.

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.

Own community strategy and execution for developer (supply) and customer (demand) audiences. Manage social channels, virtual and in-person events, partnerships, ambassador programs, and cross-functional alignment. Track community KPIs and deliver monthly performance briefings to drive engagement, retention, and top-of-funnel impact.

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.

Lead discovery with stakeholders to map workflows and controls, write clear problem statements and delivery-ready requirements (user stories, acceptance criteria), partner with product and engineering, support UAT and testing, maintain traceability and auditability, and use AI tools responsibly to accelerate analysis and documentation.

The Research Engineer role involves building and improving deep learning systems, focusing on data quality, dataset and RL environment design, and collaborating with researchers to meet high standards for AI applications.