PopVax is an Indian full-stack biotech building first-in-class and best-in-class vaccines and cancer immunotherapies using machine learning-driven 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 Virus, Strep A, and adult pulmonary TB, broadly-protective best-in-class vaccines against COVID-19, influenza, and HPV, and precision immunotherapies against hard-to-treat solid tumours such as liver cancer and pancreatic cancer.

Our work is funded by Vitalik Buterin’s Balvi Fund, the Gates Foundation, the US Biomedical Advanced Research and Development Authority (BARDA), and Renaissance Philanthropy. Our first vaccine program will begin a Phase I clinical trial in the US in mid-2026 conducted & sponsored by the National Institutes of Health (NIH). We intend to put 6 novel vaccines and immunotherapies into Phase I clinical trials over the next 3 years.

No matter the job title, each person’s role at PopVax is ultimately focused on helping take safe & effective new medicines that represent a step-change improvement on the current standard-of-care to the people who need them as fast as possible. If you want to work 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!

Our antigen design strategy for our current vaccine candidate, and our intended strategy for future vaccines, extensively uses a wide variety of protein design and engineering techniques, from old-school brute force mutation guided by free energy approximations and molecular dynamics simulations to ML-based approaches like constrained hallucination, in-painting, and diffusion for de novo structure generation, and protein language models for sequence embedding and optimization. 

We have seen up close that there are incredible capabilities in computational protein design just waiting to be unlocked that will make possible the focusing and fine-tuning of immune responses to enable an entirely new class of vaccines and therapeutics. We have already seen some impressive results from our work – early versions of our first vaccine candidate perform 10-120x better than existing mRNA vaccines in pseudovirus neutralization assays against key COVID-19 variants – and our results improve every week as we iterate in a tight in silico to in vivo loop far more rapidly than was ever possible in the past. 

In doing so, we stand on the shoulders of the good folks at DeepMind (AlphaFold), the Institute for Protein Design at the University of Washington (Rosetta/RFDesign), the AlQuraishi Lab at Columbia, the Possu Huang Lab at Stanford, the OpenFold consortium, and many others who have pioneered these techniques over the last decade and, with particularly breathtaking speed, over the last couple of years.

We want to employ these approaches, as well as new ones we invent, in service of an ambitious agenda – the rapid design and validation of broadly-protective vaccines against all key families of pandemic potential pathogens, which we see as an essential tool to preempt the emergence of new zoonotic pathogens and, if we get this right, perhaps even prevent not one, but many future pandemics from happening in the first place.

We seek to do this while ensuring that the benefits our work brings are made available to the entire global population as soon as possible – it’s right in the name, Population-scale Va[x]ccines – rather than being unevenly distributed based on wealth and geography, as many of today’s great medical advances unfortunately are.

We’re looking for a small number of exceptionally competent and insatiably curious computational protein designers to join us on our mission. To qualify for this position, a candidate must have used a variety of computational tools to design or engineer proteins, and the ideal candidate will have familiarity with SOTA machine learning methods in the field. They must be able to write code at a level sufficient to write and train their own model implementations, as well as customize existing models for our needs, though they need not be expert software engineers or machine learning scaling specialists. We will look to pair our protein designers with staff software engineers and machine learning scientists to accelerate work where necessary. 

We intend to allow for a relatively open culture of publication of both methods (via academic papers, pre-prints, and blog posts) as well as (where feasible) code and model weights. We also want to give individuals hired in this position the ability to pursue a research agenda independent of short-term company goals for some portion of their working hours and will be setting up support mechanisms for this kind of work where possible. For candidates from academia who would like to keep a foot in that world, we have premier academic institute partners with whom we may be able to negotiate a part-time appointment (for sufficiently qualified candidates). 

While we will always prefer candidates who are able to work on site at our Hyderabad lab, we are willing to consider exceptionally qualified remote work candidates for this position, supplemented with site visits to Hyderabad.

Join us!

P.S. – want to apply but worried you don't quite meet the requirements? If you believe you have enough background knowledge to quickly get up to speed, feel free to apply and explain this in the relevant experience field of the application.