Senior/Staff Machine Learning Engineer (Search & Recs)

BioRender, the 'Figma/Canva for scientists', is seeking a Senior/Staff Machine Learning Engineer to join their Search & Recommendation Team. This is a pivotal role as the first ML Engineer/Applied Scientist on the team, focused on accelerating scientists' ability to create beautiful and effective figures through advanced ML and AI systems.

The position offers an opportunity to work on cutting-edge ML innovations in areas including Ranking, Natural Language Processing, Information Retrieval, Graph Learning, and Reinforcement Learning. The successful candidate will be instrumental in building and optimizing search and recommendation systems that directly impact the core illustrator, the most heavily used part of the BioRender application.

Founded in 2018 and based in Toronto, BioRender has grown to a team of 205 people, demonstrating significant scale and stability. The company's mission is to accelerate the world's ability to learn, discover, and communicate science by democratizing science communication.

The role combines both technical depth and business impact, requiring expertise in modern ML technologies while maintaining a strong product focus. Key responsibilities include designing multi-quarter ML initiatives, overseeing search engine optimization, and collaborating across teams to deliver measurable results.

The ideal candidate will bring 6+ years of experience, with deep expertise in ML engineering, particularly in information retrieval and recommender systems. They should be comfortable with both traditional keyword-based search and modern vector-based retrieval algorithms, along with major deep learning frameworks.

This remote position offers competitive compensation ranging from $160K to $256K, reflecting the senior level and strategic importance of the role. The opportunity to work with cutting-edge ML technologies while contributing to scientific advancement makes this an exciting position for experienced ML engineers passionate about making a meaningful impact in scientific communication.