Remote Biomedical Informatics SME

Position: Remote Biomedical Informatics SME - 58854

Overview

Based in San Francisco, California, Turing is the world’s leading research accelerator for frontier AI labs and a trusted partner for global enterprises deploying advanced AI systems. Turing supports customers in two ways: first, by accelerating frontier research with high-quality data, advanced training pipelines, plus top AI researchers who specialize in coding, reasoning, STEM, multilinguality, multimodality, and agents; and second, by applying that expertise to help enterprises transform AI from proof of concept into proprietary intelligence with systems that perform reliably, deliver measurable impact, and drive lasting results on the P&L. Role Overview: We are seeking highly skilled Biomedical Informatics Subject Matter Experts (SMEs) to join our team in developing rigorous, high-quality evaluation questions designed to assess and challenge advanced AI models. The primary objective is to create benchmark questions that test the boundaries of AI capabilities across diverse bioinformatics domains. This role requires deep technical expertise combined with the ability to craft precise, unambiguous questions with well-defined evaluations. The ideal candidate will possess not only strong theoretical knowledge but also practical experience in designing assessment materials that have deterministic, verifiable outcomes.

Responsibilities

• Design and develop challenging bioinformatics coding questions that push the limits of AI model capabilities.
• Create automated, coding-based evaluations with unit tests that objectively verify correctness.
• Build comprehensive evaluation rubrics with 7–10 distinct criteria for each bioinformatics question.
• Ensure every question is self-contained, well-defined, and completely unambiguous, including all domain rules and edge cases.
• Develop problems where there is exactly one correct answer or outcome that can be deterministically validated.
• Translate theoretical bioinformatics concepts into concrete, measurable outputs.
• Enforce performance requirements with explicit constraints and tests to prevent inefficient or brute-force solutions.

Required Qualifications

Educational Background

• Master's degree of experience in biomedical data processing and interpretation.
• Ongoing PhD with 3+ years of experience in biomedical data processing and interpretation.
• PhD (completed) in Biomedical Informatics, Bioinformatics, Computational Biology or a closely related field (preferred).

Technical and other skills

• Strong proficiency in biomedical data processing, analysis, and interpretation

, including working with large‑scale datasets.

• Experience with bioinformatics tools and pipelines (e.g., NGS workflows, multi‑omics, etc.).
• Solid programming skills in Python / R
• Experience working in Linux/Unix

, Docker and version control.

• Strong communication and collaboration skills for working in interdisciplinary research teams

. Candidates must demonstrate deep, expert-level knowledge in at least TWO (2) of the following five core bioinformatics domains: 1. Biomedical Image Processing

• Processing of Radiology imaging: CT, MRI, PET/SPECT, Ultrasound imaging
• Automating cell counting, identifying cell types, and analyzing tissue structures in pathology slides
• Computational segmentation and boundary inference
• Medical image formation, reconstruction & enhancement
• Noise modeling and statistical denoising
• Image sampling theory and resolution limits
• Intensity standardization and harmonization
• Geometric transformations and spatial calibration
• Multi-modal image registration and alignment
• Morphological and topological shape analysis
• Fusing data from different modalities (MRI, PET, Genomics) for a comprehensive view to tailor treatments
• pattern recognition and predictive modeling

2. Biomedical Signal Processing

• Cardiovascular/hemodynamic signals (ECG, PPG, BP, HRV, etc)
• Ocular/visual system signals (EOG, blink/saccade analysis)
• Respiratory signals (airflow, effort; sleep/disordered breathing)
• Nonlinear, Statistical, and Adaptive Signal Analysis
• Multichannel, Spatial, and Connectivity Signal Analysis
• Machine Learning and Data‑Driven Biomedical Signal Analysis
• Clinical Monitoring, Wearable, and Translational Signal Processing

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