Bioptimus

People Operations

• Own contracting, payroll coordination, employee records, and the day-to-day questions and issues that arise.
• Be the first and trusted point of contact for everyone at Bioptimus on anything People, and the approachable face people feel comfortable coming to.
• Own both the design and the running of our core People initiatives: performance reviews, onboarding and probation, company policies, employee surveys, salary grids, and compensation strategy.
• Bring fresh, well-judged ideas suited to a startup in a hybrid environment.
• Quickly take ownership of the company's hardest people moments: coaching managers through underperformance and leading exit and termination processes. Work closely with Legal where needed.
• Be the steady, trusted partner managers come to when conversations are hard.
• Over time, identify and develop new and emerging managers, coaching them to become more effective leaders.

People Operations Lead

• Owning all People administration: contracting, payroll, answering employee questions, and fixing the issues that come up.
• Designing and running our key HR initiatives: performance reviews, onboarding and probation, policies, employee surveys, salary grids, and compensation strategy.
• Coaching managers through difficult conversations, managing underperformance, and leading exit and termination processes. Working with Legal where needed.
• Identify and develop new managers, coaching them to become more effective leaders.

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.