Jobs at Turing

Build machine learning systems that detect and de-identify sensitive information across text and image data. Develop data pipeline validation, adversarial test sets, regression gates, replacement pipelines, and audit processes. Measure precision, recall, leak rates, false negatives, and replacement consistency while investigating data quality issues across pipeline stages. The role requires strong Python, SQL, ML evaluation, privacy compliance knowledge, and experience with NER or similar detection systems.

Own Turing’s AI ecosystem partner channel, driving partner-sourced revenue, co-selling, referrals, pipeline, and enterprise account planning. Manage partner programs including onboarding, deal registration, certifications, tiers, funding, training, reporting, business reviews, and executive engagement. Coordinate competing technology partners fairly, ensure delivery commitments are staffed, and collaborate across sales, delivery, and industry teams. Build case studies, grow channel revenue, and potentially develop a partner team.

Lead a hands-on engineering team delivering custom AI and data solutions for enterprise clients. Own end-to-end delivery, including coding, data operations, architecture, infrastructure, planning, quality reviews, and process optimization. Allocate work, coordinate cross-functional partners, manage performance, mentor team members, and ensure rapid, high-quality execution under demanding deadlines. The role requires strong software engineering, data fluency, and technical leadership in a fast-paced startup environment.

Build and operate production systems supporting frontier AI research, including backend services, data pipelines, evaluation harnesses, agent workflows, sandboxed code execution environments, and internal research interfaces. Own systems end to end across design, deployment, infrastructure, CI/CD, reliability, debugging, and production operations. Collaborate with researchers and quality teams to ensure correctness, reproducibility, and reliable model evaluations.

Conduct research on frontier AI systems and develop datasets, experiments, prototypes, tooling, evaluation frameworks, and reinforcement learning environments. Investigate synthetic and agentic data generation, post-training, model understanding, benchmarks, and evaluation. Collaborate with research, engineering, product, and operations teams to translate findings into scalable AI applications, while contributing to technical publications, open-source work, mentorship, and the broader AI research community.

Conduct original research to develop frontier STEM benchmarks, evaluations, synthetic data methods, hallucination and reliability techniques, and agentic science workflows. Responsibilities include identifying research gaps, designing experiments, building evaluation pipelines, validating results, constructing benchmarks, and publishing findings. The role requires strong knowledge of modern LLMs, scientific judgment, quantitative reasoning, Python, technical writing, and independent research execution. This is an in-office position five days per week in San Francisco, Palo Alto, or Seattle.

Conduct frontier AI research and build research-grade datasets, prototypes, tooling, reinforcement learning environments, benchmarks, and evaluation frameworks. Design rigorous experiments, train and evaluate models, investigate synthetic and agentic data generation, and translate findings into scalable products. Collaborate with research, engineering, product, and operations teams, communicate technical insights, contribute to publications and open-source work, and mentor peers. This is a fully onsite role based in San Francisco, Palo Alto, or Seattle.

Lead enterprise AI deployments from problem discovery through production. Architect and build agentic AI systems using orchestration, tool use, memory, RAG, and multi-step workflows. Develop the surrounding APIs, services, data pipelines, integrations, and distributed systems while ensuring reliability, evaluation, security, observability, latency, scalability, and cost efficiency. Work directly with senior customer stakeholders, make architecture decisions, write production code, and provide hands-on technical leadership.

Conduct original research to evaluate, train, and improve frontier AI systems. Responsibilities include developing STEM benchmarks, synthetic data methods, reliability and hallucination evaluations, verification techniques, and agentic science workflows. The role involves identifying research gaps, designing rigorous experiments, building evaluation pipelines, publishing findings, and proposing new research directions across reasoning, AI evaluation, data generation, and AI-for-science.

Lead enterprise AI deployments from problem discovery through production. Build agentic AI systems using orchestration, tool use, memory, RAG, retrieval, and multi-step workflows. Develop supporting APIs, services, data pipelines, integrations, and distributed systems while owning reliability, evaluation, observability, security, latency, scalability, and cost. Work directly with senior customers, make architecture decisions, write production code, and deliver measurable business impact.

Conduct original research to evaluate, train, and improve frontier AI systems. Design benchmarks, synthetic-data methods, reliability and hallucination evaluations, verification techniques, and agentic science workflows. Identify research gaps, formulate hypotheses, build experiments and evaluation pipelines, publish findings, and develop new research directions across STEM and AI. The role requires strong Python skills, quantitative reasoning, technical writing, and expertise with modern LLMs.

Conduct frontier AI research and build research-grade datasets, prototypes, tooling, reinforcement learning environments, benchmarks, and evaluation frameworks. Investigate synthetic and agentic data generation, post-training, model understanding, and model capabilities. Design rigorous experiments, analyze results, and translate findings into scalable products and AI systems while collaborating across research, engineering, product, and operations teams.

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.

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.

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 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.