Technical Co-Founder

Salary DKK 0 - DKK 40,000

Equity To be negotiated

Mission

BioPilot is a SaaS platform that exists to help save and improve lives by making advanced cell therapies and biologics easier and faster to develop.

Clinicians and researchers cannot push the limits of science if their experiments live in scattered spreadsheets, scripts, and manual notes. BioPilot is building the 'operating system' for cell therapy researchers: a platform that standardizes complex protocols, captures data cleanly, and makes it possible to learn across thousands of experiments, not just one lab.

If we succeed, we will directly support researchers in developing new treatments for, for example, Type 1 Diabetes, Parkinson’s disease, and various cancers. The upside is scientific, ethical, and financial. This sits at an underdeveloped intersection of lab software, data infrastructure, and high-value therapeutics.

You will join as a true technical co-founder to help make this real.

Stage and funding

BioPilot is pre-funding. The MVP is under active development and no external capital has been raised yet. No salaries are paid at this stage.

What BioPilot is

BioPilot is a SaaS platform for cell therapy researchers that focuses on:

Standardized experiment planning and protocol templates
Ontology-driven data models for inventory, cells, media, and assays
Directly integrated web tools for flow cytometry, image analysis, and other downstream analytics
Clean data pipelines from instruments and files into a consistent data model

The current MVP is being built with FastAPI, Pydantic, PostgreSQL, and a modular tools architecture. The next step is to harden this into a robust, scalable product and expand the set of tools.

The role

You will:

Own and evolve the FastAPI backend that powers BioPilot
Design and maintain Pydantic models for experiments, protocols, ontologies, and user entities
Build data pipelines from lab outputs (FACS, scRNA-seq, imaging, counters, etc.) into a well-structured database
Lead ontology modeling for cell types, reagents, assays, and protocols, including integration with existing standards (for example OBO ontologies)
Implement web scraping pipelines to pull information from public databases, protocols, and literature
Drive architecture decisions around modular tools, access control, performance, and reliability
Set engineering standards: testing, CI, deployment, and documentation

Expected commitment: this is a full-time, founder-level role, not a side project.

Must-have skills

Strong experience with Python in production systems
Solid knowledge of FastAPI
Strong practical experience with Pydantic for data validation and API schemas
Experience with data engineering:, ETL pipelines, PostgreSQL, Linux systems
Experience with web scraping
Comfortable taking full ownership of features from idea to deployed service
Experience with Git and GitHub

Nice-to-have

Experience in pharma or biotech, especially cell therapies, omics, or lab informatics
Hands-on work with ontology modeling or semantic data (OBO, OWL, RDF, etc.)
Experience with Docker and basic cloud deployment (AWS / GCP / Azure / Scaleway)
Experience with secure multi-tenancy applications
Experience with IT security
Experience designing analytical tools that run in the browser

Who you are as a co-founder

You want to build something that directly supports the development of new therapies, not another generic app
You are ready to work hard and iterate fast
You care about code quality and long-term maintainability
You can communicate clearly with other subject-matter experts across different STEM fields
You want real ownership, not a token title

Compensation and upside

This is a co-founder position, not an employee role. The company has not raised external funding yet. The exact structure will be agreed together and documented formally (vesting, cliff, etc.). A starting point could be:

Option A: High-equity, no-salary start (pre-funding)

30–40% equity
No salary until significant external funding or revenue supports a sustainable founder salary

This option is for someone who is ready to join now, at the current pre-funding stage.

Option B: Significant equity plus founder-level salary (post-funding)

10–20% equity
Founder-level salary in line with data science roles in Copenhagen, once minimal funding is in place and for a meaningful period (for example 24 months)

Option B is intended for someone who wants to commit as a co-founder but prefers to start once the first external funding or equivalent revenue is secured. In that case, terms are agreed in advance and you join as soon as the funding is in place.

What you get

A chance to directly influence which therapies can reach patients
Large, meaningful equity and direct influence on product and architecture
Close collaboration with a founder who understands data, biology, IT, and hardware
A focused, no-nonsense environment
Broad exposure and connections to the pharmaceutical industry
Free coffee

If you want to build something that can contribute to real treatments for patients and has clear upside, this is for you.

Let's have an informal talk!

For more information or questions please contact us at lorenz@biopilot.net

Perks and benefits

This job comes with several perks and benefits

Remote work allowed

Free coffee / tea

Flexible working hours

Skill development

Equity package

Gym access

Working at
BioPilot

See company profile

Only 7.9% of new medicin is approved for phase 1 clinical programs - life saving cures are being scrapped because of poor reproducibility. Today, researchers designing complex in vitro and in vivo experiments jump between many tools that do not talk to each other. Protocols are written in free text, naming is inconsistent, raw data is scattered across drives, and inventory and cell banks are tracked separately. This makes it hard to reproduce experiments, compare results across projects, trace which cell material went into which batch, and satisfy regulatory expectations. Small and mid-sized labs often cannot afford enterprise platforms like Benchling or Dotmatics, so they manually glue everything together and rarely get to use advanced analytics or AI. BioPilot solves this by standardising the full early-to-in-vivo CTR pipeline in one product. Users can register and template in vitro experiments (differentiation, expansion, cryopreservation, device testing, etc.) and in vivo studies (animals, groups, dosing, housing, ethics documents) using fixed ontologies. This gives every experiment a structured schema and a precise schedule of readouts, barcodes and data types to collect. Raw data (FACS, scRNA-seq, imaging, counters, bioreactor logs, etc.) is then automatically linked back to the right experiment, condition and sample. On top of this, BioPilot offers: • Inventory and cell bank tracking that makes it trivial to trace which vial, edit or expansion batch was used where. • Live bioreactor monitoring with alarms (email / phone) to prevent losing expensive batches. • A growing library of free analytical tools, including automated FACS gating, scRNA-seq pipelines and image analysis that can feed results straight back into the experiment records. • A global ontology service for vendors, instruments, cell lines, species and process terms, with a roadmap for a public API. Because everything is structured and queryable, BioPilot makes it much easier to compare experiments, run Bayesian optimisation on process parameters, and let AI suggest next-step experiments. The pricing is kept aggressively low (free tier plus a simple paid tier based on storage), so smaller biotech and academic groups can access capabilities that are normally reserved for organisations with large software budgets. In short, BioPilot aims to become the operating system for cell therapy R&D: a single, affordable platform where experiments are planned, executed, monitored and analysed in a way that is standardised enough for automation and flexible enough for real-world biology.