About Turing: 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 Domain Expertise Requirements Candidates must demonstrate deep, expert-level knowledge in at least TWO (2) of the following five core bioinformatics domains: 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 Quantitative imaging feature extraction Fusing data from different modalities (MRI, PET, Genomics) for a comprehensive view to tailor treatments pattern recognition and predictive modeling Biomedical Signal Processing Cardiovascular/hemodynamic signals (ECG, PPG, BP, HRV, etc) Neural/brain electrical signals (EEG, EP/ERP; seizure/cognitive analysis) Neuromuscular/motor signals (EMG, motor unit decomposition; prosthetics/rehab) 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 Molecular Omics based Personalized Medicine Omics based risk / disease prediction (GWAS, PRS, bulk, single-cell RNA-seq, proteins / metabolite signatures associated with diseases) Risk Prediction and Prognosis Modeling Molecular patient subtyping Liquid Biopsy and Circulating Biomarkers (ctDNA, cfRNA/miRNA, MRD, etc.) Causal Inference Mechanistic Inference (ATE/CATE, Trial emulation and counterfactual modeling) Cross-modal data fusion (omics + EHR + imaging / signals) Longitudinal Patient Modeling and Disease Progression Patient Stratification and Subtyping (molecular subtypes, endotypes) Electronic Health Records and Clinical Data Science Drug Discovery Repurposing Treatment response prediction Structure-based drug design (Molecular docking, binding site analysis, Virtual screening workflow) Protein structure analysis (PDB/mmCIF handling) Chemoinformatics ( Ligand-based screening, molecular fingerprinting, 2D/3D data handling and chemical space analysis) QSAR ADMET Modeling Drug–target interaction prediction Drug sensitivity prediction in disease models Repurposing via omics/network associations Predictive Modeling for Drug Discovery Molecular Dynamics Simulation of Biomolecules (QM, DFT, stability, reactivity, PCA analysis ) Software / Utilities Tool installation environment management (docker/conda/pip) Format conversion/validation (FASTA/FASTQ, BAM/VCF, PDB) GUI-driven analysis workflows (step-by-step runs) Workflow automation (scripts wrapping GUI tools) Visualization tools (genome browsers, 3D viewers) Container/VM execution and troubleshooting Batch processing and report generation Pipeline interoperability checks Reproducibility packaging (configs, manifests) Debugging typical bioinformatics tool errors Regulatory Science and Clinical Policies FDA/EMA regulatory pathways Clinical Practice Guidelines Standards of Care (e.g., NCCN, AHA, etc.) Diagnostic Biomarker Therapeutic Use Constraints Medical Scenario Reasoning (case vignettes, Differential diagnosis) Data Content Compliance Ethical Use, Safety, and Harm Prevention Model Use Governance Auditability Policy-Aligned Language Communication Clinical validation and cohort generalization Privacy-preserving learning Perks of Freelancing With Turing: Work in a fully remote environment. Opportunity to work on cutting-edge AI projects with leading LLM companies. Potential for contract extension based on performance and project needs. Offer Details: Commitments Required : At least 30 hours per week. (We have 2 options of time commitment: 30 hrs/week or 40 hrs/week) Engagement type : Contractor assignment/freelancer (no medical/paid leave) Duration of contract 2 months; [expected start date is next week] Evaluation Process: Take home Assessment on Biomedical Informatics (2-6 hours) Delivery discussion(15-30mins) After applying, you will receive an email with a login link. Please use that link to access the portal and complete your profile. Know amazing talent? Refer them at turing.com/referrals, and earn money from your network.
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