Company: Turing

Responsible for developing digital and high-speed electronic hardware from requirements definition and component selection through schematic design, PCB layout, and design verification. Main tasks listed: Analyze system requirements and develop hardware specifications and circuit architectures; Select processors, microcontrollers, FPGAs, memories, interface ICs, power-management components, and supporting circuitry; Design schematics for embedded systems, digital interfaces, communication boards, and high-speed electronic products; Develop multilayer PCB layouts with appropriate stack-up planning, reference planes, controlled impedance, differential-pair routing, and timing constraints.

We are seeking an experienced Electrical Engineering or Computer Science expert to lead a team of technical trainers developing realistic, terminal-based scientific and engineering tasks for a frontier AI training and evaluation initiative. In this role, you will oversee a pod of approximately 5–10 trainers, review technical tasks for accuracy, reproducibility, and evaluation quality, and ensure tasks reflect authentic computer science, software systems, and electrical engineering workflows. Main tasks listed: Lead and mentor a pod of technical trainers, ensuring consistent quality, productivity, and timely delivery of scientific and engineering tasks; Review technical tasks end to end, including problem statements, codebases, circuit netlists, hardware description languages (HDL), datasets, computational environments, reference solutions, and automated tests; Validate tasks involving high-performance computing (HPC), parallel computing, algorithms, distributed systems, compilers, operating systems, databases, ML systems, GPU computing, circuit design, signal processing, power systems, and embedded systems; Ensure tasks reflect realistic software engineering, research, and electrical engineering workflows requiring meaningful multi-step technical reasoning.

Support the daily resource planning and deployment of 1,800+ contributors across multiple AI training projects. Reporting to the Supply Manager, you will allocate contributors, maintain accurate workforce data, monitor capacity, and coordinate with Project, Training, and Onboarding Leads. Main tasks listed: Allocate and move contributors based on project demand; Coordinate access, training, and instructions for resource movements; Maintain accurate deployment, status, performance, and capacity data; Monitor project capacity, throughput, onboarding, and offboarding.

Own resource planning and deployment for a large-scale AI training program, managing 1,800+ contributors across multiple projects. You will align capacity with client demand, oversee resource tracking systems, identify supply risks, and lead a team of Supply Coordinators to ensure smooth 24/7 operations. Main tasks listed: Make data-driven resource allocation and deployment decisions; Forecast capacity gaps and guide recruiting and fulfillment teams; Manage and improve centralized resource and throughput tracking systems; Integrate data from recruiting, onboarding, performance, and project delivery.

Serve as the subject-matter expert for complex quality decisions in AI annotation projects. You will guide QA Leads, resolve ambiguous cases, lead calibration efforts, and use audit insights to improve guidelines, training, and overall data quality. Main tasks listed: Convert complex client guidelines into clear internal instructions; Validate project setup before production begins; Mentor and calibrate QA Leads and annotation teams; Lead calibration sessions and maintain reviewer consistency.

Manage day-to-day quality operations for multimodal AI annotation projects. Reporting to the Quality Manager, you will lead QA teams, monitor quality performance, resolve issues quickly, and ensure deliverables meet client standards within tight turnaround times. Main tasks listed: Lead Senior QA Leads and QA Leads across shifts; Set review priorities, sampling plans, and quality coverage; Implement audits, scoring rubrics, and calibration processes; Track accuracy, agreement, error rates, review throughput, and turnaround time.

Lead the quality strategy for large-scale AI training data and annotation programs. You will define quality standards, manage quality teams, oversee audits, and ensure accurate, consistent, and timely delivery in a fast-paced environment with tight turnaround times. Main tasks listed: Define quality standards, metrics, and targets across data types; Build and lead Quality Leads and QA teams; Develop audit, sampling, calibration, and escalation frameworks; Lead client quality reviews and manage quality-related escalations.

We are seeking coding experts with strong language skills to support AI training and evaluation projects. You will apply your programming expertise to review code, solve technical problems, and improve the accuracy and clarity of AI-generated responses in your language of expertise. Main tasks listed: Write, review, debug, and optimize code for various programming tasks; Evaluate AI-generated code for correctness, efficiency, readability, and adherence to instructions; Create and review coding prompts, solutions, and technical explanations in your language of expertise; Ensure technical responses are clear, linguistically accurate, and use appropriate programming terminology.

Turing invites Healthcare Experts to join projects that fine-tune AI models. If you enjoy applying clinical knowledge, analyzing complex medical scenarios, and providing clear, structured feedback, this is your chance to put your expertise to work in a new way. Your healthcare experience will help shape how the next generation of AI understands and reasons about medical topics and patient care. Main tasks listed: Review and evaluate AI-generated responses to medical and healthcare scenarios; Apply a structured rubric to assess clinical accuracy, reasoning, completeness, and patient-safety considerations; Identify gaps, edge cases, unsafe recommendations, and blind spots in medical reasoning; Provide detailed annotations and written feedback to refine AI models.

We are seeking an analytical researcher and red-teaming specialist to build evidence-based web-research benchmark tasks that reliably expose weaknesses in state-of-the-art (SOTA) AI models (e.g., Claude Opus 5 Max). In this role, you will discover hard-to-surface facts, build precise research tasks around them, test them directly against leading LLMs, and diagnose the exact failure mechanisms in the model’s search and reasoning paths. Main tasks listed: Obscure Fact Discovery: Deeply research niche topics to identify factual ground truths that cannot be surfaced via simple, direct search queries; Source Verification: Rigorously verify facts against primary or highly credible sources, cross-check for contradictory evidence, and document exact source URLs; Task Construction: Draft clear, answerable benchmark tasks designed specifically to expose structural weaknesses in model research strategies; Red-Teaming & Model Testing: Execute tasks against designated SOTA models to confirm they reliably trigger intended model failures.