Education Requirements:

• MS/PhD degree in Computer Science, Statistics, Applied Mathematics, Computational Biology, Computational Chemistry or other related subject

• Will also consider BS degrees in these areas for candidates highly qualified across other requirements or with significant work experience

Experience Requirements:

• 2+ years of experience on machine learning teams, ideally at a start-up

• 4+ years of ML research experience in industry or academia, with strong familiarity with PyTorch

Other Requirements:

• Track record of contributing to novel methods for state-of-the-art Deep Learning (in industry or through publications) including large-scale Transformers, Graph Neural Nets, ConvNets, etc.

• Experience with Python is required

• Relevant experience working in a Linux/UNIX environment with basic data engineering and scripting abilities

• Ability to understand business problems and how to build models that can quickly drive value, while prioritizing your research efforts accordingly

• Clearly communicate modeling setup and results at various levels of abstraction

Responsibilities:

• Scientifically direct the design and training of large-scale, state-of-the-art Deep Learning systems

• Develop novel architecture and training paradigms to lead the industry in unsolved scientific problems

• Collaborate with content experts from other domains (e.g., chemistry, physics, biology) to enable innovative feature-engineering and novel cross-disciplinary approaches

• Actively contribute to top-tier ML conferences and journals and attend core ML conferences to stay connected with the community and current trends

Education Requirements:

• PhD in Chemical Biology, Proteomics, or related field

Experience Requirements:

• 5+ years in industry experience focused on small molecule drug discovery at pharmaceutical or biotech companies, with a track record in leading small molecule proteomic initiatives

Other Requirements:

• Proven experience in proteomics research and small molecule drug discovery

• Track record in designing and executing complex scientific experiments

• Computational affinity, worked with computational biologists

• Experience in managing a team of PhD researchers

• Familiarity with AI applications in proteomics and drug discovery

• Strong budget management skills

• Excellent communication skills, adept at summarizing scientific progress for various audiences

Responsibilities:

• Drive the scientific strategy, aligning our proteomics research with AI-driven drug discovery initiatives

• Lead and nurture a team of PhD researchers, promoting a culture of innovation and discovery

• Oversee lab operations and facilities, maintaining an environment that fosters excellence and safety

• Plan and steer ambitious proteomics experiments, making key decisions to shape our scientific trajectory

• Prudently invest in state-of-the-art lab hardware to keep us on the cutting edge of proteomics research Bridge the gap between lab teams and computational workstreams, fostering a synergistic environment that maximizes data utility Manage and optimize the lab budget, ensuring resources are strategically allocated Provide regular updates on scientific progress, keeping stakeholders informed and inspired

Education Requirements:

• MSc or PhD degree in computational biology, bioinformatics or a related field

Experience Requirements:

• Possess mixed academic and/or industrial experience, preferably in the biotech industry

Other Requirements:

• Strong background in computational structural or systems biology

• Strong programming skills in Python, R and/or other languages

• Experience building reproducible and scalable computational biology workflows

• Experience designing and applying machine learning models

Responsibilities:

• Develop pilot capabilities and create large-scale data pipelines from the ground up

• Collaborate with other teams across VantAI and partnering organizations to achieve project goals by contributing code, analyses, benchmarks, optimizations and ideas to ongoing research-driven projects

• Integrate biological insights into the next generation of AI/ML models for drug discovery

• Validate and interpret computational predictions by working closely with experimentalists