Bioptimus

Full-stack Engineer

• 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.

Technical Lead – Product

Data Scientist

• 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

Senior Clinical Data Manager

• 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

Technical Lead, Product

• Bridge the gap between unstructured, real-world data, and AI models
• Structure clinical datasets
• Write reproducible code
• Enforce incoming data quality control
• Design data dictionaries and ontologies
• Participate in technical conversations with external partners
• Translate ambiguous source data into harmonized, AI-ready assets
• Map 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 frameworks
• Write production-grade Python code for data cleaning and harmonization
• Audit data to find missing variables, anomalies, and hidden biases
• Utilize clinical data progression metrics

• Operate at the intersection of data engineering, clinical science, and partner collaboration across two strategic domains:
• Technical conversations with external partners (hospitals, research institutions, CROs/CMOs) and dive into the details of diverse clinical data structures.
• 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) and validation frameworks for incoming partner data.
• Write production-grade Python code to automate data cleaning and harmonization tasks.
• Practical understanding of how clinical data is generated in the real world.
• Identify anomalies and hidden biases in incoming data.

Operations

• 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.

Data Operations Lead

• 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.