Rezo Therapeutics is hiring a

Staff Machine Learning Engineer, Computational Biology and AI

San Francisco, United States
About Rezo Therapeutics
Rezo is a different kind of biopharma company.  Our mission is to dramatically increase the success rate of drug development by building a disease-agnostic, fully-integrated, network biology platform that will redefine our understanding of human disease and ability to treat it.   Our Founders and members of the Board are a group of esteemed, world-class scientists from the University of California, San Francisco: Nevan Krogan, Kevan Shokat, Sourav Bandyopadhyay, Natalia Jura, as well as renowned biotech executives George Scangos and Norbert Bischofberger. These leaders bring with them a strong track record of amazing discoveries and scientific breakthroughs, leading to very successful companies and many FDA approved drugs. 

Capitalizing on our Series A of $78M from top-tier investors, we are leveraging a unique integrated approach, combining capabilities in genetics, proteomics, structural biology and AI to generate deep biological insights and novel therapeutic approaches. A list of seminal studies leading to our company's formation, published in the journal Science, can be found here.

Located in San Francisco’s vibrant Mission Bay neighborhood, Rezo is in close proximity to UCSF, numerous urban amenities, and outdoor recreational opportunities.  We recognize that realizing our mission depends as much on our people as it does on our science and are building a workplace that fosters collaboration, respect, and diversity where contributing to your team’s success is valued as highly as individual achievements.   If you’re passionate about team science and breaking down barriers to progress in drug development, please consider joining us! 

The Opportunity 
We are seeking a highly enthusiastic and motivated Staff Machine Learning Engineer to join the Rezo Computational Biology and AI team.  Becoming a member of this team is an excellent opportunity to join Rezo at an early stage and help build the Computational Biology and AI function and the company from the ground up.

What You'll Do:

  • Advise on AI strategy and serve as technical lead for ML-based approaches to integrate large-scale gene and protein interaction datasets to power Rezo’s Sequence to Systems to Drugs (SSD) platform and other core company initiatives (e.g. computational chemistry and protein structure prediction)
  • Operate as an individual contributor initially while building a team of ML and software engineers
  • Design, develop, test, deploy, maintain, and enhance large-scale software solutions
  • Adopt and develop data engineering methodologies including feature identification and integration, data pipelining, feature engineering, data munging, and analysis using methods that can translate from research to production
  • Collaborate across biology and computational biology to develop strategy for platform buildout, systematic platform-driven data generation, data management and mining, and buildout of interfaces and tools
  • Manage and analyze experimental data, interpret results, and prepare necessary materials for internal and external meetings, regulatory filings, and publications
  • Contribute to and balance multiple projects/studies in parallel in a fast-paced environment

About You:

  • Ph.D. in computer science or a related technical field plus 4+ years work experience OR B.S in computer science or a related technical field plus 8+ years work experience
  • Demonstrated industry expertise in implementing deep learning algorithms and using deep learning frameworks (e.g. TensorFlow, PyTorch, JAX)
  • Proven track record of scientific excellence as evidenced by a strong publication and/or patent record in the ML field (e.g. NeurIPS, ICML, ICLR, and/or top journals in the sciences)
  • Demonstrated expertise in data analysis including proficiency in programming languages, software best practices, and data structures/algorithms
  • Hands-on experience working on computational biology, systems biology, drug discovery, or computational chemistry datasets, highly desirable
  • Previous experience in graph neural networks, a plus
  • Excellent communication, organizational, and interpersonal skills
  • Strong mentoring skills required, prior management experience is a plus
  • Demonstrated ability helping set scientific direction and priorities for projects
  • Able to own and solve a problem, even if it’s ambiguous
  • Able to partner with peers in different functions and contribute to cross-functional projects with a collaborative attitude
  • Able to work with minimal direction

Benefits at Rezo
Medical (HMO or PPO), dental, and vision insurance
Discretionary time off policy
Company holidays (including summer and winter shutdown)
401(k) retirement savings program
Commuter / Mass Transit Benefit Program
Healthcare Flexible Spending Account (FSA)
Dependent care Flexible Spending Account (FSA)
Parental leave
Competitive compensation
Flexible work schedule

At Rezo we value a diverse, equitable, inclusive workplace and provide equal employment opportunity to all persons without regard to race, color, sex, gender identity, gender expression, religion, age, national origin, ancestry, citizenship, physical or mental disability, medical condition, family care status, marital status, domestic partner status, sexual orientation, genetic information, military or veteran status, or any other basis protected by federal, state or local laws.  This statement covers all facets of employment and is especially important when it comes to growing our team.  

If you are wondering if you’ll belong at Rezo, or are worried that you don’t meet 100% of the qualifications for this role, take a chance on us and yourself — please apply!

SF Fair Chance Ordinance
Pursuant to the San Francisco Fair Chance Ordinance, Rezo considers qualified applicants with arrest and conviction records for employment.

All Rezo employees are required to be fully vaccinated against COVID-19 and to provide proof of vaccination prior to their first day, with limited exceptions.  This policy is grounded in scientific and clinical evidence and has been established with the health and safety of our workplace and community in mind.
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