We combine cutting edge AI, broader technologies, and human expertise to re-engineer a better model of healthcare
👋 Please click ▶️ to expand
🌳 Backend Opportunities
We began rolling out our Telehealth platform and services to the UK in 2013 and rapidly grew to more than 24 million patient-subscribers globally. In 2020 we entered the US market, quickly realized that US Healthcare can only be changed from within, and opened the doors of our new Austin, TX Engineering HQ in 2021 🚀
As a founding member of US Engineering you’ll work on some of Healthcare’s most interesting challenges alongside seasoned engineersfrom companies like Apple, Expedia, Paypal, Blue Apron, Bright Health, and Workrise who have already joined our cause.
When you have a moment please review the information below. If you’re curious, I’d love to chat!
Our Comp philosophy is simple.. Remove Ambiguity. Reward Achievement.
Salary MID Points
New Hire Equity
Base + Equity
Senior Software Engineer
Staff / Lead Software Engineer
There are no rows in this table
Base + Equity
(Base Salary * Equity % / 4) + Base Salary. Assumes no change in our stock price.
Mid-Point: The salaries above reflect the middle of our pay ranges. The total compensation you receive at time of offer is primarily determined by how well you do in our One Team interview process (below).
Salary location differential: Salaries are based on the Austin, TX market. Please increase salary mid-points 15-20% if your primary residence is California, Seattle or New York City
Where is the bonus? We’ve decided to do away with the performance based bonus. Instead, we have added that amount to your Base Salary. While this might seem odd, it’s a Win-Win; your bonus isn’t subject to interpreted performance and our annual costs don’t reflect a large one-time increase.
New Hire Equity Grant & Refreshes
How new hire RSUs are calculated
Babylon equity grants are made in the form of Restricted Stock Units (RSUs). RSUs are a form of equity compensation issued by Babylon. We grant RSUs at no cost to you.
Babylon’s Board meets once a quarter to determine that quarter’s “grant price”. Generally speaking, the grant price equals the 3 month trailing average of our stock price.
Dividing your new hire equity grant value by the grant price determines the number of RSUs you receive.
New hire RSU vesting schedule
Your RSUs vest over a four-year period, while you’re employed with Babylon.
RSUs have no value until vesting is achieved.
At vesting, the RSUs are “settled” in shares of Babylon’s Class A common stock. As a result, if you satisfy the vesting period, you will receive shares, which will always have some value.
For new hires, 25% of a grant’s RSUs vest one year from the vesting commencement date. The remaining 75% vest evenly over the following 12 quarters.
Your vesting commencement is not your start date. It is the date when our Board determines the grant price for the current quarter.
Vested RSUs are yours to keep without any conditions.
If you leave Babylon before full vesting in a given equity grant, you forfeit your unvested RSUs.
Eligibility for annual RSU refreshes commences on your 2nd anniversary and, at the discretion of your manager, are awarded at the end of our annual review cycle (May through June). Your refresh amounts are primarily determined by your performance. Refreshes do not have a “cliff”; they start vesting quarterly immediately.
Babylon 2022 US Benefit Guide Final.pdf
We believe that taking time away from work is essential for Babylonians to rest and recharge, which in turn will help you to continue dreaming big, building fast and being brilliant. For months now you have been hearing about our new approach to time off and while we finalize the details, we are delighted to introduce BBLN-Leave from January 1st, 2022 and provide the key principles below.
Flex Time off
Who does this apply to? Babylonians who work full-time and don’t work to a predetermined schedule or on specific days
What is Flex Time off and what does this mean for me? You will be able to take paid time off when you need it, without waiting for PTO time to be accrued or managing a fixed number of days off. This will give you the flexibility and autonomy to take what you need to suit your individual circumstances
How will this work? We won’t be limiting the time you take off as leave each year within reason; we expect Babylonians to exercise judgement in the performance of their roles and to work with their managers when requesting time off under this new approach. You and your manager will need to align in advance to ensure there is sufficient coverage for business continuity and your time off is approved and recorded in ADP
One Team Interview Process
One interview gives you access to all open engineering roles
60 Min Zoom: Systems Design
45 Min Zoom: Behavioral & Motivational
Here’s how it works..
After your interviews we send your panel’s feedback to all engineering hiring managers
Interested HMs request a 30 minute chat with you
We send you a short team profile for each HM’s team containing a summary of the work they do, size of the team, upcoming initiatives, etc.
You let us know which teams interest you
We schedule 30 minute exploratory chats with your chosen teams
You decide which teams are the strongest fit
We present your verbal offer
🕜 Time from initial Recruiter chat to multiple offers: 2 to 2.5 weeks
📖 About Babylon
🧭 Our Mission
2500 years ago, in the ancient city of Babylon, folks needing medical advice would gather in the town square and share thoughts on treatments for common illnesses. This is one of the earliest examples of democratizing healthcare - It’s no surprise that Babylonians enjoyed the longest life expectancy across the ancient world
Our mission is to take this simple ideology, apply radically modern technology, and put an accessible and affordable health service in the hands of every person on Earth🌎 🙌.
To make healthcare accessible, we deliver it through the devices people already own.
To make healthcare affordable, we use technology to help ease the burden on our doctors.
By automating their routine tasks, we allow doctors to focus on what they do best: give care to the patients who need it most.
We also use our tech to step in early and help people become more informed about diseases — so they can make informed decisions and stay healthier for longer.
Uniquely positioned to demystify and unify two critical trends
VALUE BASED CARE
✅ Movement away from broken fee-for-service model
✅ Aligns system around proactive care
❌ Not scalable traditionally
❌ Challenge addressing many patient types
✅ Leverages technology-driven efficiencies
✅ Bringing care to the patient vs patient to care
❌ Not scaled to address holistic care
❌ Shifts site of care vs addressing overall care and cost
🙌 Our People
🗺️ Product Roadmap
Babylon started in the UK in 2013 with the lofty goal of combining cutting edge AI and broader technologies with human expertise to re-engineer a better model of healthcare.
Our app is in both app stores and has a 4.8 star rating. We are more than 24 million patient-subscribers strong across the UK, East Asia, and the US. Babylon patients and physicians have access to...
Symptom Wizard our advanced Machine Learning chatbot guides our patients through the diagnostic process, capturing and understanding their symptoms along the way and providing pinpoint recommendations at the end of the decision tree.
Video Appointment Service allows patients to have a physical and secure in-app chat remotely with a doctor, at home, at work, or on the move.
Health Check Service the preventive side of our app focusing on proactive value-based-care. Patients can upload their personal medical records, their family medical history, intended goals & actions. This entire layer of information is fed by real-time biometric feeds from any device that can transmit Patient Health Information (PHI)
Developing Nations Strategy
We've mirrored 90% of our in-app functionality for devices that only accept email and SMS.
For countries like Africa, our service is crucial. We have 2.5 million patient subscribers in Rwanda alone and are using this success as a template for other developing nations.
Joseph Enguehard, Dan Busbridge, Adam Bozson, Claire Woodcock, Nils Hammerla
To address their temporal nature, we treat EHRs as samples generated by a Temporal Point Process (TPP), enabling us to model what happened in an event with when it happened in a principled way. Our proposed attention-based Neural TPP performs favourably compared to existing models, and provides insight into how it models the EHR, an important step towards a component of clinical decision support systems.
Published in 2020 | ML for Health Workshop, NeurIPS 2020
Adam Baker, Yura Perov, Katherine Middleton, Janie Baxter, Daniel Mullarkey, Davinder Sangar, Mobasher Butt, Arnold DoRosario
We performed a validation study of the accuracy and safety of the babylon AI triage system and human doctors using a set of identical clinical cases. Overall, we found that the AI system is able to provide patients with triage and diagnostic information with a level of clinical accuracy and safety comparable to that of human doctors.
Published in 2020 | Frontiers in Artificial Intelligence Medicine and Public Health
Yuanzhao Zhang, Robert Walecki, Joanne Winter, Felix Bragman, Sara Lourenco, Chris Hart, Adam Baker, Yura Perov and Saurabh Johri
AI-driven digital health tools often rely on estimates of disease incidence or prevalence, but obtaining these estimates is costly and time-consuming. We demonstrate that context-aware machine learning models can be used for estimating disease incidence. These methods are quicker to implement than traditional epidemiological approaches. We therefore suggest it complements existing modelling efforts, where data is required more rapidly or at larger scale. This may particularly benefit AI-driven digital health products where the data will undergo further processing and a validated approximation of the disease incidence is adequate.
Published in 2020 | Frontiers in Artificial Intelligence Medicine and Public Health
Claudia Schulz, Josh Levy-Kramer, Camille Van Assel, Miklos Kepes and Nils Hammerla
A promising application of AI to healthcare is the retrieval of information from electronic health records (EHRs), e.g. to aid clinicians in finding relevant information for a consultation or to recruit suitable patients for a study. This requires search capabilities for beyond simple string matching, including the retrieval of medical concepts (diagnoses, symptoms, meditations, etc) related to the one in question. We open-source a novel medical concept relatedness benchmark, which is six times larger than existing datasets and consists of concept pairs that co-occurr in EHRs, ensuring their relevance for medical information retrieval from EHRs.
Jonathan G. Richens, Ciarán M. Lee & Saurabh Johri
Machine learning promises to revolutionize clinical decision making and diagnosis. In medical diagnosis a doctor aims to explain a patient’s symptoms by determining the diseases causing them. However, existing machine learning approaches to diagnosis are purely associative, identifying diseases that are strongly correlated with a patients symptoms. In this paper we show that this inability to disentangle correlation from causation can result in sub-optimal or dangerous diagnoses. To overcome this, we reformulate diagnosis as a counterfactual inference task and derive counterfactual diagnostic algorithms. We compare our counterfactual algorithms to the standard associative algorithm and 44 doctors using a test set of clinical vignettes. While the associative algorithm achieves an accuracy placing in the top 48% of doctors in our cohort, our counterfactual algorithm places in the top 25% of doctors, achieving expert clinical accuracy. Our results show that causal reasoning is a vital missing ingredient for applying machine learning to medical diagnosis.
Vitalii Zhelezniak, Aleksandar Savkov, April Shen, Nils Hammerla
Some of the top approaches to semantic textual similarity rely on various correlations between word embeddings, including the famous cosine similarity. We show that mutual information between dense word embeddings, despite being difficult to estimate, is another excellent candidate for semantic similarity and rivals existing state-of-the-art unsupervised methods.
Stoilos, Giorgos and Juric, Damir and Wartak, Szymon and Schulz, Claudia and Khodadadi, Mohammad
The success of logic-based methods for comparing entities heavily depends on the axioms that have been described for them in the Knowledge Base (KB). Due to the incompleteness of even large and well engineered KBs, such methods suffer from low recall when applied in real-world use cases. To address this, we designed a reasoning framework that combines logic-based subsumption with statistical methods for on-the-fly knowledge extraction.
Published in 2020 | European Semantic Web Conference
We create various large-scale datasets for testing whether embeddings correctly encode the similarity between medical terms and test existing state-of-the-art embeddings on these datasets. Our results reveal that existing embeddings cannot adequately represent medical terminology. Our new datasets are thus challenging new benchmarks for testing the adequacy of new medical embeddings in the future.
Knowing that a disease is highly correlated with symptoms, or a drug highly correlated with recovery, is not enough, and basing medical decisions on such information can be dangerous. To truly begin to revolutionise healthcare, AI must learn to distinguish cause and effect. Our work solves this by utilising new physics-inspired ideas about what it means for one variable to cause another, and showing how causal relationships in one dataset limit the possibilities in other overlapping datasets. To illustrate our algorithm, we apply it to breast cancer data, showing how to extract causal relations between two important features despite the fact that they were never measured in the same dataset.
Vitalii Zhelezniak, Aleksandar Savkov, April Shen, Francesco Moramarco, Jack Flann, Nils Y. Hammerla
We push the limits of word embeddings on semantic textual similarity tasks by introducing DynaMax, a novel unsupervised non-parametric similarity measure based on word vectors and fuzzy bag-of-words. This method is efficient and easy to implement, yet outperforms current baselines on STS tasks by a large margin.
Douglas et al. Published in 2017 | NIPS Workshop, NIPS 2017
🌎 Scaling Globally
We cover more than 24 million people globally and provide over 6,000 consultations per day.
In 2013 we were founded in the UK where we partner both with private healthcare providers and also our National Health Service. Currently we have around two and a half million registered users in the UK.
Outside of the UK we have also begun expanding in a number of other regions over the last couple of years. This includes South East Asia where we’ve partnered with Prudential who are a large healthcare provider in that region. There we have partnered with them to roll out our Healthcheck product, which is branded as the “Pulse” by Prudential service to 13 countries across South East Asia including Vietnam, Malaysia and Laos. Currently around 12 million Prudential users have access to this service.
We also launched in the US in 2020, where we’re currently operating in 7 states have around 7 million users currently have access to our service. This is going to be a huge growth area for us in the future and we’re currently scaling our engineering function both in Austin, Texas and the UK.
But some of the most interesting and meaningful work that we’ve done has been in Africa. This is something that many people at the company and particularly the founder is very passionate about, because we really want to be able to offer our services not only to countries that already have a good healthcare infrastructure in place, but also to countries that are perhaps lacking slightly more in that respect.
The work that we’ve done in Africa so far has predominantly been in Rwanda. Here we’ve partnered with the Rwandan government to provide and offer our services to around 2.5 million people currently.
It’s been particularly beneficial to remote regions of Rwanda that might usually struggle to access healthcare services. We’re able to do this because we tailor our service in Rwanda, so rather than video appointments, we mainly provide text and call based appointments and services. The main reason for this is the lack of infrastructure and that only 15% of the adult population in Rwanda have access to smartphones. Therefore by tailoring our service it allows us to reach and help many more users in that region.
This has been a great blueprint of what we’d like to achieve in other developing nations and is something we’re looking to expand on in the future.
On October 22nd, 2021 we became a publicly traded company.
Thank you Babylonians and our partners for another amazing quarter, adding 88,000 value based members in the US entirely organically, to bring the total number of our managed care lives to more than 440,000. We surpassed $80m recurring revenue in this January, and as a result increased our full year revenue expectations to up to $1bn.
While our continued growth may seem extraordinary in the healthcare universe, as seen in the graph below which was presented at the J.P. Morgan San Francisco conference, it is not unlike the levels of the many disruptive digital innovators such as Amazon, Netflix, Tesla, or Airbnb, who also experienced superior growth in their “take-off” years.
As happened in those sectors, it is now clear we are witnessing the dawn of a structural digital overhaul in healthcare.”