Bioptimus

• Build the product that puts our foundation models for biology into the hands of scientists and engineers • Design, build, test, and ship features spanning back-end services and front-end interfaces for our B2B biology products • Take features from idea to production and own them in operation, balancing speed with quality and maintainability • Design and maintain robust, scalable services and APIs (REST, gRPC) that compose our packaged models into product capabilities • Build data workflows and work fluently with our core stack (Python, SQL, and related technologies) • Use agentic coding tools (e.g., opencode, Claude) to accelerate delivery • Work closely with Product Engineers and platform/infra engineers to align what we build with real scientific and customer needs.

• Operate at the intersection of data engineering, clinical science, and partner collaboration • Participate directly in technical conversations with external partners (hospitals, research institutions, CROs/CMOs) • Translate ambiguous source data into harmonized, AI-ready assets • Map and align diverse clinical data to industry-standard biomedical ontologies with an emphasis on clinical oncology and immunology data • Design, build, and maintain data dictionaries, schemas, and metadata models • Establish, automate, and enforce data quality control (QC) and validation frameworks • Write production-grade Python code to automate data cleaning and harmonization tasks • Understand how clinical data is generated in real-world settings • Actively audit data to find missing variables, anomalies, and hidden biases • Recognize important data in clinical trials related to oncology/immunology

• Bridge the gap between unstructured, real-world data, and frontier AI models • Serve as the technical link during conversations with global partners to standardise and harmonise data pipelines • Structure clinical datasets within the STELA program • Write reproducible code, enforce incoming data QC, and design data dictionaries and ontologies • Participate directly in technical conversations with external partners (hospitals, research institutions, CROs/CMOs) • Translate ambiguous source data into harmonized, AI-ready assets • Map and align diverse clinical data to industry-standard biomedical ontologies • Design, build, and maintain data dictionaries, schemas, and metadata models • Establish, automate, and enforce data quality control (QC) frameworks • Write production-grade Python code to automate data cleaning and harmonization tasks • Actively audit data to identify missing variables, anomalies, and hidden biases • Familiarity with cancer progression metrics.

• Build product that puts foundation models for biology into the hands of scientists and engineers • Own features end to end and turn cutting-edge research into reliable, well-crafted products • Work in close partnership with Product Engineers • Design, build, test and ship features spanning back-end services and front-end interfaces for B2B biology products • Set engineering patterns and standards for the surfaces you own • Mentor other engineers through code review and pairing • Collaborate closely with Product Engineers and platform/infra engineers

• Bridge the gap between unstructured, real-world data, and frontier AI models • Structure clinical datasets and write reproducible code • Establish, automate, and enforce data quality control (QC) and validation frameworks • Participate in technical conversations with external partners • Design, build, and maintain data dictionaries, schemas, and metadata models

Role Description We are looking for a Clinical Expert to support Bioptimus’ clinical and scientific strategy for digital twin and data generation programs. In this role, you will work closely with the science, data, and partnerships teams to support clinical prioritization, scientific interpretation, academic collaborations, and pharma engagement across our research programs. You will contribute clinical and translational expertise to help identify the most important clinical questions and modalities to focus on, support the interpretation of model outputs, and advise on partnerships and research programs aligned with Bioptimus’ data and scientific goals. This is a part-time engagement with the possibility of becoming full-time. What You'll Be Doing - Clinical Strategy: - Support the identification of the most important clinical questions and modalities to prioritize across training and validation cohorts. - Provide clinical input on tissue, drug class, disease, and modality prioritization. - Support the selection of clinical metadata, endpoints, and patient-level annotations required to maximize downstream model utility. - Identify and prioritize clinically relevant tasks including response prediction, toxicity prediction, biomarker discovery, indication expansion, and trial enrichment. - Scientific Interpretation: - Provide clinical and biological review of model outputs and scientific results. - Work closely with the science team to support results review and contribute to external-facing scientific narratives. - Translate model behavior into clinically credible language for pharmaceutical, key opinion leader, and regulatory audiences. - Data & Academic Partnerships: - Support connections with academic investigators running relevant clinical studies, including early-phase studies, novel-agent programs, immuno-oncology programs, and multimodal cohorts. - Advise on academic partnership structures that align research collaborations with Bioptimus’ data needs, including retrospective and prospective studies. - Pharma Engagement: - Support discussions with pharmaceutical R&D teams where relevant. - Advise on the positioning of model claims for translational medicine and clinical development audiences. Qualifications - MD/PhD with active or recent Principal Investigator experience designing and analyzing clinical studies, particularly studies involving translational endpoints, biomarker sub-studies, and multimodal cohorts. - Deep expertise in onco-immunology, including IO mechanisms, response and resistance biology, tumor microenvironment biology, IO combinations, and understanding of pharmaceutical pipelines and unmet clinical needs. - Working understanding of and genuine interest in machine learning and foundation model approaches applied to pathology, transcriptomics, and multimodal data integration. - Ability to work closely with scientific, clinical, and external stakeholder groups. - Strong communication skills with the ability to contribute to scientific discussions and external-facing narratives. - Comfortable operating in a startup environment with ambiguity and evolving priorities. How to Stand Out - Pharma R&D experience through an industry role or substantive pharmaceutical collaboration history. - Existing academic or hospital network in onco-immunology. - Experience designing and analyzing studies involving translational endpoints, biomarker sub-studies, or multimodal cohorts. - Familiarity with ML or foundation model approaches in pathology, transcriptomics, or multimodal biology. The Candidate Journey - Screening: Once you have applied, the hiring team will review your application to determine if your work experience and skills align with the necessary proficiencies of this position. - Hiring Manager (30 min): A discussion with the Hiring Manager focused on your clinical and translational research background, scientific expertise, and motivation for collaborating with Bioptimus. - Strategic Case Study Discussion (60 min): A collaborative discussion with members of the scientific and technical teams focused on clinical strategy, translational medicine, multimodal biology, and the application of foundation models in oncology and immunology. - Offer: Following the completion of the interviews, our hiring team will make a final decision and will be in touch to share the outcome of your interviews. If the team would like to move forward, the recruiter will discuss the details of our proposed offer with you. - Onboarding: We are happy to have you joining the team. Once you have accepted and signed your offer, we will be in touch to begin the process of onboarding you to Bioptimus. Why This is a Unique Opportunity - Be part of a trailblazing team working at the intersection of AI, biotech, and biomedical research. - Take on a high-impact leadership role, shaping the future of biomedical AI through strategic data partnerships. - Work in a collaborative, innovation-driven environment with top researchers and industry experts.

• Data Partnership Operations & Lifecycle Management: Own the operational lifecycle of external data partnerships following contract signature. Act as the primary operational and technical point of contact for hospitals, biobanks, CROs, and research laboratories. Coordinate onboarding, data delivery timelines, and stakeholder communication to ensure successful execution of partnership milestones. • Data Transfer & Infrastructure Coordination: Manage secure biomedical data transfers using cloud infrastructure and standardized transfer protocols. Coordinate access management, encryption, and ingestion workflows across cloud storage systems (AWS S3, SFTP, APIs, direct upload pipelines). Ensure incoming datasets are delivered, validated, and tracked according to internal governance standards. • Clinical & Multi-Omics Data Harmonization: Collaborate with internal technical and product teams to define and maintain harmonized data models and metadata standards across complex clinical and multi-modal datasets. Organize and maintain relationships between clinical metadata and associated omics or imaging assets, including genomics, transcriptomics, spatial biology, and pathology data. • Pipeline Operations & Automation: Work closely with engineering and data teams to configure and maintain lightweight ingestion and QC pipelines. Identify operational bottlenecks and repetitive workflows and convert them into scalable systems, scripts, templates, dashboards, or automation tools that improve operational efficiency and visibility. • Data Quality Oversight: Coordinate automated and manual quality control checks across incoming datasets. Identify missing data, inconsistencies, corruption, or metadata mismatches and work directly with external partners to resolve issues. Ensure data integrity, traceability, and version control throughout the ingestion process. • Operational Tracking & Reporting: Maintain a centralized “single source of truth” for all incoming datasets, including ingestion status, completeness, QC status, and milestone tracking. Build and maintain reporting dashboards and operational tools to provide visibility into project progress, ingestion velocity, and operational risks. • Cross-Functional Collaboration & Communication: Partner closely with Data Science, Engineering, Legal, and Partnership teams to align operational execution with business and scientific priorities. Communicate technical issues clearly to both scientific collaborators and non-technical stakeholders. Provide regular updates on operational risks, blockers, and delivery progress. • Site Visits & External Partner Engagement: Conduct periodic visits to partner hospitals, biobanks, and laboratories to support onboarding, troubleshoot technical or operational bottlenecks, and strengthen long-term collaborations.

• Own the operational lifecycle of external data partnerships following contract signature. • Manage secure biomedical data transfers using cloud infrastructure and standardized transfer protocols. • Collaborate with internal technical and product teams to define and maintain harmonized data models and metadata standards across complex clinical and multi-modal datasets. • Work closely with engineering and data teams to configure and maintain lightweight ingestion and QC pipelines. • Coordinate automated and manual quality control checks across incoming datasets. • Maintain a centralized “single source of truth” for all incoming datasets, including ingestion status, completeness, QC status, and milestone tracking. • Partner closely with Data Science, Engineering, Legal, and Partnership teams to align operational execution with business and scientific priorities. • Conduct periodic visits to partner hospitals, biobanks, and laboratories to support onboarding, troubleshoot technical or operational bottlenecks, and strengthen long-term collaborations.

• Own the operational lifecycle of external data partnerships following contract signature. Act as the primary operational and technical point of contact for hospitals, biobanks, CROs, and research laboratories. • Manage secure biomedical data transfers using cloud infrastructure and standardized transfer protocols. Coordinate access management, encryption, and ingestion workflows across cloud storage systems (AWS S3, SFTP, APIs, direct upload pipelines). • Collaborate with internal technical and product teams to define and maintain harmonized data models and metadata standards across complex clinical and multi-modal datasets. • Work closely with engineering and data teams to configure and maintain lightweight ingestion and QC pipelines. Identify operational bottlenecks and repetitive workflows and convert them into scalable systems, scripts, templates, dashboards, or automation tools that improve operational efficiency and visibility. • Coordinate automated and manual quality control checks across incoming datasets. Identify missing data, inconsistencies, corruption, or metadata mismatches and work directly with external partners to resolve issues. • Maintain a centralized “single source of truth” for all incoming datasets, including ingestion status, completeness, QC status, and milestone tracking.

• Own the operational lifecycle of external data partnerships following contract signature. • Manage secure biomedical data transfers using cloud infrastructure and standardized transfer protocols. • Collaborate with internal technical and product teams to define and maintain harmonized data models and metadata standards. • Work closely with engineering and data teams to configure and maintain lightweight ingestion and QC pipelines. • Identify missing data, inconsistencies, corruption, or metadata mismatches and work directly with external partners to resolve issues. • Maintain a centralized “single source of truth” for all incoming datasets, including ingestion status, completeness, QC status, and milestone tracking. • Partner closely with Data Science, Engineering, Legal, and Partnership teams to align operational execution with business and scientific priorities. • Conduct periodic visits to partner hospitals, biobanks, and laboratories to support onboarding.