Today, machine learning (ML) technologies are becoming key tools in medical diagnostics, making it possible to speed up and improve the process of analyzing patient data. Thanks to ML, doctors and medical startups can quickly and accurately process large volumes of medical images, such as X-rays and CT scans, to improve diagnosis and quality of care. This is the kind of specialist our client was looking for — a medical startup developing an ML-based platform for automatic recognition and analysis of X-ray and CT images.
Customer request
The client was a promising medical startup that is developing an innovative platform for diagnosing diseases based on the analysis of X-ray and CT images using ML and Computer Vision (CV).
To successfully launch the product, the client needed a Senior ML Engineer with experience in developing neural networks and skills in working with images. The main requirements included:
- Experience in developing and optimizing image recognition algorithms using neural networks;
- Work in large corporations with streamlined processes to be able to transfer best practices to startups;
- Living in Europe or being ready to relocate to work closely with the team;
- Availability of specialized education from the list of educational institutions presented (about 15 universities);
- Active participation in ML specialized competitions, such as Kaggle, and a high rating in these events (as an indicator of skills and motivation).
Difficulties encountered
- Education and specialized requirements
The company preferred candidates from a limited list of leading technical universities, which significantly limited the choice. In addition, the client was interested in specialists who had experience and high results in ML competitions, especially in image recognition tasks on Kaggle. - Work experience in large companies with streamlined processes
The client wanted to see a candidate with experience in large technology companies, where best practices in development and quality control have already been implemented so that the candidate could apply his skills in a startup environment. However, given the initial stage of the project, many specialists preferred to pursue their careers in an already stable corporate environment. - Features of a medical startup
Although the project was at an early stage, the startup needed a specialist who could create accurate models for analyzing medical images, as well as develop a system that met high requirements for the quality and accuracy of diagnostics.
Recruitment process
Together with the client, we created a portrait of an ideal candidate, focusing on ML engineers from top universities with experience in medical imaging and experience in Kaggle competitions. After analyzing the requirements and preparing suitable strategies, our recruiters identified the main search sources and compiled a list of “donor” companies, including medical technology and large companies with strong ML teams.
During the first week of work, we focused on directly searching for candidates from an active market and presented the client with two qualified candidates. One of them stood out for his high position on Kaggle, entering the top 20% of participants, and for his experience in a medical company, where he implemented functionality similar to a client's request. This was fully in line with the client's expectations.
The result of our work
The recruitment process took 17 working days. Our recruiter actively worked with the candidate at all stages, helping to build trust and show all the benefits of working in a medical startup. We processed more than 70 resumes, sent about 40 personalized offers, and conducted 4 interviews with the most suitable candidates. As a result, a candidate suitable for the client's skills and expectations accepted the offer, despite the availability of other offers.
To consolidate the candidate's interest in the project, we organized additional meetings with medical consultants from the startup and the project's service station, which allowed the candidate to better understand the significance of his future work and his contribution to the development of medicine. Taking into account our experience, we also offered assistance with relocation and adaptation, which accelerated the candidate's readiness to take up duties.
Upon completion of the selection, the client noted not only a significant acceleration in product development, but also an improvement in the accuracy of the medical image recognition system. A month later, the candidate successfully started working, and in a short time made a valuable contribution to the development of the innovation platform.
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