Note: The job is a remote job and is open to candidates in USA. SDLC Technologies is seeking a Clinical Manager to serve as Clinical AI Evaluators and Testers. The role involves executing structured evaluations against complex clinical rubrics and performing stress-testing to identify edge-case failures before models are launched to the public.

Responsibilities

• Structured Grading: Reviewing prompt-response pairs and scoring them across dimensions like clinical accuracy, safety, and various health risk domains
• Dynamic Sprints: Participating in "sweeps" where they dynamically converse with the model to probe specific health risks (e.g., attempting to get the model to prescribe medication or diagnose a complex condition)
• Providing Clinical Rationale: Writing clear, concise, and scientifically backed justifications for why a model response was graded as unsafe or inaccurate

Skills

• Medical Degree: MD, MBBS, or local equivalent from an accredited institution
• Board Certification: Candidates must be Board-Certified in one of the following priority specialties: Primary Care / Family Medicine / Internal Medicine, Pediatrics, Obstetrics & Gynecology, Cardiology, Oncology, Psychiatry / Clinical Psychology, Emergency Medicine / Urgent Care
• Licensure: Active, unrestricted clinical licensure in good standing in their practicing geography (US, UK/EU)
• Active Practice: Direct, current practice in patient care
• Hands-on LLM Experience: Active experience using generative AI platforms (e.g., Gemini, ChatGPT, Claude) for clinical workflows, research, or everyday queries
• Awareness of AI Limitations: Understanding of the inherent limitations of LLMs, including hallucinations, sycophancy, bias, and the risks of generating unsafe clinical advice
• Prompting Skills: Ability to write clear, structured, and complex prompts to interact with models dynamically
• Adversarial Probing: Ability to adopt different user personas to stress-test the model
• Rubric-Based Grading: Experience working with complex, multi-dimensional rubrics and comfort with aligning their grading to strict 'Anchor Examples'
• Structured Grading: Reviewing prompt-response pairs and scoring them across dimensions like clinical accuracy, safety, and various health risk domains
• Dynamic Sprints: Participating in 'sweeps' where they dynamically converse with the model to probe specific health risks
• Providing Clinical Rationale: Writing clear, concise, and scientifically backed justifications for why a model response was graded as unsafe or inaccurate
• Advanced Degrees: An additional advanced degree in a relevant field (e.g., Master of Public Health (MPH), MS/PhD in Medical Informatics, Computer Science, or Behavioral Science)
• Multilingual: Fluency in multiple languages and deep understanding of cultural health beliefs and regional clinical guidelines
• Prior AI Safety Work: Previous experience designing, implementing, or participating in AI safety evaluations, Trust & Safety workflows, or RLHF for medical models

Company Overview

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