Top Tech Jobs & Startup Jobs

Own product strategy, roadmap, prioritization, and execution across talent, marketplace, vetting, onboarding, trust, and platform workflows. Translate ambiguous problems into requirements, use data and experimentation to guide decisions, shape user experiences, and partner with engineering, design, data, QA, and operations to deliver scalable products.

Owns KYC and identity verification quality by adjudicating high-risk cases, reviewing sanctions and AML screenings, auditing verification decisions, identifying fraud patterns, and improving policies, processes, documentation, and reviewer training. The role requires independent judgment across global, high-volume operations and involves calibration, root-cause analysis, quality metrics, and program improvements in a remote environment.

Leads investor relations, IPO readiness, M&A, strategic investments, and capital allocation. Builds the company’s M&A playbook, ventures program, investor narrative, and related cross-functional processes. Partners with the CFO, CEO, board, product, engineering, and go-to-market teams on transactions, valuation, diligence, negotiations, integration, strategic planning, and build-buy-partner decisions.

Owns enterprise AI engagements from signed SOW through production deployment, adoption, renewal, and expansion. Leads client relationships, steering committees, delivery scope, timelines, staffing, risk management, P&L, margins, and change orders. Manages Forward Deployed Engineer pods and partners with technical leaders to deliver measurable business impact in regulated financial-services environments. Requires executive presence, technical fluency in AI systems, financial-services delivery experience, and onsite availability in the New York metro area.

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.