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 About HotSpot

Nature tends to control the activity of proteins in the cell not through the active site but through allosteric regulatory sites. HotSpot is focused on finding modulators of these allosteric sites in well validated disease targets in order to drug proteins previously perceived hard to drug or undruggable.

Through our proprietary technology platform called SpotFinderTM, we are uncovering privileged allosteric sites – called regulatory hotspots - that can be exploited for small molecule drug discovery.  Regulatory hotspots are allowing sought-after targets to be drugged for the first time while delivering molecules with exquisite selectivity, in vivo potency and attractive drug-like properties.

To date, HotSpot has identified regulatory hotspots across over 100 targets spanning many pathways of pharmaceutical interest. HotSpot has delivered the first and only allosteric inhibitors for PKC-theta and S6 kinase, offering important new ways to treat autoimmune and metabolic disease.

HotSpot is supported by a syndicate of leading healthcare investors based in the US and Europe. For more information, please see our website at


 Who are we looking for?

 You are a highly motivated, creative and collaborative machine learning engineer - or a bioinformatician or computational chemist with significant experience in applying machine learning techniques. You want to play a pioneering role in identifying pockets on proteins that are critical to function and can serve as the focus for drug discovery efforts. You have demonstrated an ability to develop novel machine learning methods that go beyond putting together of existing code or toolboxes, and to apply problem-solving skills to complex issues.

You will be joining a seasoned team of drug hunters with an excellent track record in drug discovery. As a HotSpotter, you are comfortable thinking outside the box and breaking convention. You are curious about the world and contribute beyond your precise role description. You can think strategically in one moment but then roll your sleeves up to troubleshoot an experimental protocol in the next. You are action orientated and extraordinary at getting things done. You communicate openly with your colleagues and you hate office politics.


Our recruiting will not be interrupted. Applications are being processed, online tests sent, video interviews conducted, challenges sent, interview days take place as far as possible. 

We are looking forward to welcoming new colleagues as soon as the situation eases. Please understand that as long as this date is not yet clear, we can only make offers with an open starting date.

We are looking forward to your application!


How HotSpot works with idalab

idalab GmbH is a close collaborator to HotSpot and has helped developing crucial parts of SpotFinder ™. idalab is HotSpot’s partner of choice when it comes to Machine Learning, Big Data analysis, Artificial Intelligence, statistics, and development of user interfaces. The company provides office space for all HotSpot colleagues working from Germany indicating a good cultural fit. Over the last two years, idalab hosted several work summits bringing together with numerous collaborators of HotSpot resulting in productive interdisciplinary meetings.  


Your role

You will support the application of machine learning to the identification of novel allosteric sites in proteins – based on sequence and structural information – and apply these to drug discovery. You will work closely with a cross-functional team of computational chemists, biologists, and data scientists to identify areas where machine learning can make a difference.

In our Berlin office, you will work alongside a team of seasoned machine learners and data scientists from our strategic partner idalab. You will have plenty of opportunities to discuss ideas with experienced and enthusiastic colleagues, and will be fully integrated into idalab’s ML-related learning and community activities.

Your typical day will include the following activities:

  • Develop a deep understanding of cutting-edge methods applied to protein structure and pocket prediction
  • Evaluate large pocketome datasets comprising sequence and structural information
  • Develop new approaches to apply machine learning to pocket prediction and identification of regulatory pockets
  • Identify and/or build new technologies important for longer-term success
  • Build relationships with other world-class researchers in the field; establish collaborations
  • Support computational chemistry teams in the development of chemistry uniquely tailored to regulatory pockets
  • Contribute to longer-term strategy development
  • Support writing, development of publications to showcase HotSpot’s innovation in machine learning


Your background and previous experience

  • BS, MS, or Ph.D. in biology, bioinformatics, chemistry, computational chemistry, physics or similar with demonstrated history of using machine learning approaches
  • If you have MS or Ph.D. in subjects such as mathematics, computer science, statistics, engineering or similar subjects, you must have some experience in one of the areas in the section ‘Nice to have’ (see below) to fit our expectations
  • Expertise in one or more general-purpose programming languages (such as Python, C/C++, or Scala)
  • 1 - 2 years of real-world work experience in engineering robust, efficient and high-quality software for machine learning algorithms using a modern, team-oriented development environment (git, etc.)
  • Demonstrated track-record in finding novel creative algorithmic solutions to challenging problems

Nice to have

  • Experience with protein structures (X-ray, NMR) and computational chemistry software (Schrödinger, Molecular Discovery suite)
  • Experience with biological data (DNA sequences, proteomics, microscopy images, etc.) and knowledge of the relevant data bases
  • Experience with scalable machine learning, including the application to large datasets
  • Proficiency in Linux environment (including shell scripting), experience with database languages (e.g., SQL, No-SQL)
  • Familiarity with cloud computing services (AWS or GCP)



idalab GmbH, Potsdamer Strasse 68, Berlin, Germany. You will be part of HotSpot’s Berlin based team, which is co-located with idalab, our strategic partner for Artificial Intelligence and Machine Learning. 


Application process

HotSpot’s application process is standardized in four stages: (1) Online test, (2) Telephone interview, (3) Data challenge, (4) Personal introduction. First of all, you have the opportunity to prove your basic technical know-how in an online test. After you have successfully completed this first step, we would like to talk to you in a 45-minute telephone interview. Next, a data challenge awaits you, for which you send us your results (slides and code). During the final personal meeting you will have the opportunity to present your solution and meet potential colleagues. 

If you want to apply, please upload the following documents: cover letter, CV, university transcripts (with grades). PLEASE NOTE: INCOMPLETE APPLICATIONS WILL NOT BE CONSIDERED. 



Please only apply via job boards or our career page on Applications via email cannot be considered, due to data privacy obligations. If you have any questions regarding the application process or the job ad, please contact Hannah Martin: 

  • +49 (30) 814 513-24