PopVax is an Indian full-stack biotech building first-in-class and best-in-class vaccines and cancer immunotherapies using machine learning-driven protein design and relentless empiricism. We design, develop, and manufacture our own RNA medicines end-to-end because we believe great pharmaceutical science can only flourish in tight feedback loops that iterate rapidly. PopVax’s experimental work and clinical dose production is based at the RNA Foundry, our integrated R&D and GMP-capable clinical dose production facility in Hyderabad.
PopVax's north star is our goal of developing novel vaccines and therapeutics over the next decade with the cumulative ability to save 1 million lives each year – the Million Lives Mission. To that end, we are developing first-in-class vaccines against Hepatitis C, Strep A, and adult pulmonary TB; broadly-protective best-in-class vaccines against COVID-19, influenza, malaria, and HPV; and precision immunotherapies against hard-to-treat solid tumours such as liver cancer and pancreatic cancer. Beyond existing diseases, we are leveraging our high-speed platform to build rapid-response biosecurity capabilities against engineered de novo pathogens.
Our mission is funded by Vitalik Buterin’s Balvi Fund, the Gates Foundation, the US Biomedical Advanced Research and Development Authority (BARDA), Renaissance Philanthropy, and Good Ventures, with individual investments from Enveda founder Viswa Colluru and Tesla self-driving AI pioneer Dhaval Shroff. Our first program, an open-source broadly-protective COVID-19 vaccine, will begin a Phase I clinical trial in Australia in mid-2026. This is just the start – we intend to advance 6+ novel vaccine and immunotherapy programs into human clinical trials over the next three years, decisively demonstrating that world-class biotech R&D is possible in India.
No matter the job title, each person’s role at PopVax is ultimately about helping bring safe, effective new medicines that represent a step-change over the current standard-of-care to the people who need them, as quickly as possible. If you are looking for a place where the ambition is high, the learning curve is steep, and the work matters to billions, you’ll feel at home here.
If you’re excited by the idea of advancing scientific, clinical, and regulatory frontiers of vaccines and immunotherapies, spending each day developing medicines with the potential to save millions of lives, and building a generational global pharmaceutical company in India along the way – join us!
Role Overview
There's a particular kind of scientist who has always existed but never had a good name. The person who designs a novel assay at the whiteboard, builds it at the bench, writes the analysis pipeline from scratch in Python, runs the statistics properly, spots the signal in the noise, and then — because they understand both the biology and the computation deeply enough — feeds the results back into a machine learning model that designs the next round of experiments.
Most organisations split this person into three people and a committee. A biologist does the wet lab work. A bioinformatician processes the data. A data scientist builds the model. A project manager coordinates the handoffs. By the time the insight from experiment round one has been translated across all those boundaries and fed back into the design of experiment round two, weeks have passed and information has been lost at every interface.
PopVax can't afford that. We're a full-stack biotech company designing vaccines and immunotherapies using computational protein design, manufacturing them in our own GMP facility, and taking them to clinic at a pace that depends on tight, fast feedback loops between wet lab data and computational design. Our entire scientific strategy rests on the ability to run experiments, extract signal, and use that signal to design better candidates — quickly, rigorously, and without the information loss that comes from passing data across organisational boundaries between people who don't fully understand each other's work.
We need scientists who can close that loop themselves. Not in theory, not by "collaborating closely" with a computational team on the other side of a Slack channel, but by being both sides of the loop in a single person.
We call this role Full-Stack Biologist, because that's what it is.
What You'll Actually Do
You will work at the heart of PopVax's R&D operation, doing work that spans the full arc from experimental design through wet lab execution through data analysis through computational feedback — and you will own that entire arc, end to end.
Some weeks, you'll be at the bench. Other weeks, you'll be at a terminal. Most weeks, you'll be at both, because the work requires it. The biology informs the computation, the computation informs the biology, and the person best positioned to make that loop tight and fast is someone who doesn't have to hand off between the two.
Specifically, you will:
Design, build, and optimise complex assays from scratch. We need novel, high-throughput functional assays that can evaluate our vaccine and immunotherapy candidates at scale — assays that generate the kind of quantitative, structured data that computational models can learn from. You'll conceive of these assays, design them, troubleshoot them at the bench, optimise them, and scale them up. This isn't running someone else's protocol. This is building the measurement system itself, from first principles, for problems where off-the-shelf solutions don't exist.
Write the code for data ingestion and processing. When your assay generates data — and at high throughput, it will generate a lot of data — you'll build the pipeline that ingests it, cleans it, structures it, and makes it usable. From scratch, in Python or whatever the problem requires. Not by asking a bioinformatician to do it for you, and not by wrestling with Excel until it breaks. You write code. Real code, with version control and tests and documentation, that other people on the team can understand and build on.
Do the statistics correctly. This matters more than it should need to be said, but it does need to be said. You'll design experiments with appropriate controls, calculate power, choose the right statistical framework, and interpret results with the kind of rigour that distinguishes actual signal from wishful thinking. You have a strong grounding in statistics and probability — not just a passing familiarity with p-values, but a genuine understanding of experimental design, inference, and the many ways that biological data can mislead you if you're not careful.
Close the design feedback loop. This is where it gets really interesting. The data you generate in the wet lab — binding affinities, neutralisation profiles, expression levels, immunogenicity readouts — feeds directly into the computational models that PopVax uses to design the next generation of vaccine and immunotherapy candidates. Ideally, you won't just hand that data to the machine learning team. You'll be able to train models on it yourself, understand what the models are learning, and implement a therapeutic design feedback loop end to end — from wet lab data to trained model to new candidate design to the next round of experiments. If you can do this, you are exactly the person we're looking for. If you can't do it yet but you're close and hungry to learn, we still want to talk to you.
Work across the full pipeline. At PopVax, the boundaries between molecular biology, immunology, protein design, mRNA delivery, and analytical characterisation are porous by design. You'll collaborate with scientists across all of these domains, and the breadth of your own skills — spanning wet lab and computation — will make you a natural integrator across teams that might otherwise operate in silos.
Who You Are
You're a biologist who codes. Not a biologist who once took a Python workshop and can run someone else's Jupyter notebook. A biologist who writes software from scratch — data pipelines, analysis tools, visualisation scripts, and ideally machine learning models — because you've realised that computational fluency is not a nice-to-have in modern biology. It's the difference between being someone who generates data and someone who extracts insight from it.
You have deep wet lab expertise. You've spent years at the bench, you have excellent hands, and you understand the difference between an assay that works in principle and an assay that works reliably at scale. You know that the hardest part of building a novel high-throughput assay isn't the clever idea — it's the hundred small optimisation decisions that make it actually produce trustworthy data, day after day, plate after plate.
You have a strong grounding in statistics and probability. You design experiments properly. You don't confuse statistical significance with biological significance. You understand the pitfalls of multiple comparisons, the importance of proper controls, and the difference between a result that's real and a result that's an artefact of how you processed the data.
You think in systems. When you look at an experiment, you don't just see the assay — you see the entire loop from hypothesis to experimental design to data generation to analysis to insight to the next hypothesis. You want to own as much of that loop as possible, because you've seen what happens when it's fragmented across too many people: it gets slow, information gets lost, and the science suffers.
Qualifications
PhD in Biology, Immunology, Molecular Biology, Biochemistry, Bioengineering, or a related field — or equivalent research experience demonstrating comparable depth.
Deep wet lab expertise, with demonstrated ability to design, build, optimise, and troubleshoot complex biological assays.
Proficient programmer — Python at minimum, from scratch, not just scripts copied from Stack Overflow. You should be able to build a data processing pipeline, write clean and maintainable code, and work comfortably in a computational environment.
Strong grounding in statistics and probability, including experimental design, hypothesis testing, and the analysis of high-dimensional biological data.
Track record of work that spans both wet lab and computational domains — publications, projects, or roles where you personally did both.
Preferred (but not required)
Experience with machine learning, particularly as applied to biological data — training models, evaluating performance, understanding what the model is and isn't learning.
Experience building or contributing to computational feedback loops for therapeutic or molecular design (e.g., directed evolution, protein engineering, antibody optimisation, or similar iterative design cycles).
Experience with high-throughput screening, functional assays, or large-scale biological data generation.
Familiarity with mRNA biology, immunology, vaccine development, or computational protein design.
Experience in a fast-paced startup or similar R&D environment.
Reporting Structure
You'll report to the VP of Programs, Darshit Mehta, who previously worked on automating high-throughput assays at Gingko Bioworks, and collaborate with people across PopVax's computational and experimental teams. You'll also work in close proximity to the manufacturing and QC teams, because at PopVax, the distance from the bench to GMP is measured in feet, not years. Soham Sankaran, PopVax’s founder & CEO — a computer scientist who taught himself biology and built a vaccine company — will also work closely with you, particularly on the computational side of the feedback loop.
This is a role for someone who has spent their career frustrated by the artificial divide between wet lab biology and computation, and who wants to work at a company where that divide doesn't exist. If you're the person who always ends up writing the analysis code for your own experiments because nobody else understands both the biology and the data well enough to do it right — and you wish the entire company worked that way — PopVax is the place.