• Design, prototype, and evaluate applied AI solutions across natural language, vision, recommendation, and structured data domains. • Translate ambiguous business problems into well-scoped ML formulations with clear success metrics and evaluation strategies. • Stay current with the latest research in deep learning, large language models, and adjacent areas, and assess applicability to internal use cases. • Implement rigorous experimentation workflows including baselines, ablations, and statistically sound evaluation methodology. • Build production-quality training and inference pipelines using modern ML frameworks and orchestration tools. • Collaborate with ML platform engineers to ensure efficient use of compute, storage, and accelerator resources. • Optimize models for accuracy, latency, throughput, and cost based on production requirements. • Develop tooling for dataset construction, labeling, validation, and ongoing monitoring of data quality. • Partner with product, design, and domain experts to ensure model behavior aligns with user needs and policy requirements. • Implement safety, fairness, and reliability evaluations and incorporate findings into model selection decisions. • Document research findings, design decisions, and operational characteristics clearly for both technical and non-technical audiences. • Mentor engineers on applied ML methodology, evaluation rigor, and responsible deployment. • Contribute to internal knowledge sharing, reading groups, and prototype-to-production playbooks. • Influence the broader AI roadmap based on research insight, capability gaps, and emerging opportunities.

Role Description Guide research and engineering teams to close knowledge gaps in AI and data science domains. Surface nuances that distinguish expert-level work from surface-level reasoning. - Design challenging agentic tasks rooted in real-world ML, data science, data engineering, and software workflows. - Write accurate, well-documented solutions that serve as ground truth. - Evaluate AI agent outputs against your solutions. - Provide detailed written feedback capturing correctness, efficiency, and reasoning quality. - Develop and refine evaluation frameworks and rubrics for assessing agentic behavior on AI and data science tasks. - Collaborate with other subject matter experts to ensure consistency and accuracy in training data. Qualifications - Must-Have: - 3+ years of research, academic, or industry experience in Machine Learning, Data Science, Software Engineering, Computer Science, Statistics, Biology, Electrical/Mechanical/Civil Engineering, Physics, Chemistry, Mathematics, Materials Science, or other STEM background. - Demonstrated technical expertise in programming, data analysis, ML modeling, statistical methods, or computational methods. - Ability to commit to 40 hours per week during weekdays for the duration of the engagement. - Strong written communication skills and the ability to explain technical decisions clearly. - Preferred: - Prior experience with data annotation, labeling, evaluation, or human feedback collection. - Experience with LLMs, AI systems, or agentic workflows; familiarity with agentic frameworks. Requirements - Contract position - Compensation: $70–$100/hour - Location: Remote - Commitment: 40 hours/week Application Process - Upload resume - AI interview based on your resume - Submit form Resources & Support - For details about the interview process and platform information, please check: Interview Process - For any help or support, reach out to: support@mercor.com

• Serves as the visionary leader in Bioinformatics AI application development in a clinical genetic testing setting. • Provides technical guidance and hands-on support towards building company’s next-generation bioinformatics AI platform. • Identifies, prototypes, and develops state-of-the-art AI applications to revolutionize clinical testing and genomic analysis workflow. • Designs, develops, evaluates, and deploys novel AI solutions to gain valuable data insights based on the genetical, phenotypical, and clinical datasets. • Evaluates, adopts, and customizes GenAI models based on both internal and external datasets to build next-generation clinical genetic testing platforms. • Supports both internal and external data requirements by leveraging AI and GenAI capabilities to keep up with the increasing demands of the business. • Collaborates in a multidisciplinary and regulated clinical diagnostics environment with geneticists, bioinformaticians, software engineers, and IT infrastructure professionals.

• Conduct a research study with Legal professionals • Collect real-world professional scenarios to help train AI models • Submit scenarios resolved through conversation or consultation • Follow validation criteria for scenario submissions