Senior ML Ops Engineer
EEOC
AWSDockerKubernetesPython
Principal Machine Learning Scientist
BigHat Biosciences
full-time
Posted on:
Location Type: Hybrid
Location: San Mateo • California • 🇺🇸 United States
Visit company websiteSalary
💰 $233,000 - $266,000 per year
Job Level
Lead
Tech Stack
AWSPythonPyTorch
About the role
- Design and implement the next state-of-the-art generative models of antibody sequence and structure, and predictive models of antibody properties, trained on proprietary internal datasets of thousands to millions of antibodies.
- Identify opportunities for improvement in our ML tooling, and help to set strategy for ML research, driven by a strong high-level understanding of real-world drug development challenges
- Develop, refine, and deploy *de novo* design methods for generating initial hits to challenging, therapeutically interesting targets.
- Develop multi-modality, multi-objective iterative protein sequence optimization approaches to lab-in-the-loop antibody design problems for validation and deployment in our high-throughput wet lab - at BigHat success is only declared upon synthesis of real antibodies with drug-like properties.
- Maintain an in-depth understanding of the current state-of-the-art in ML-driven protein engineering, both in the literature and at BigHat.
- Share your findings at top-tier conferences and publish in leading scientific journals to advance the field of protein engineering.
- Provide technical guidance and mentorship to other ML and data science FTEs and interns.
- Provide ML expertise and support for ongoing therapeutics programs, directly contributing to the development of new drugs.
- Collaborate with our engineering team to ensure maximal efficiency in the automated deployment of our latest models to ongoing drug development programs.
- Work closely with an interdisciplinary team of drug developers, wet lab scientists, automation specialists, data scientists, etc. to identify inefficiencies or potential improvements in BigHat’s platform, and plan and prioritize ML methods development accordingly.
Requirements
- PhD in ML/CS or in the hard sciences with 5+ years experience developing and applying novel ML methods and a strong quantitative background.
- Publications in major ML conferences and/or leading journals, or extensive demonstrable track record developing and applying novel ML in industry.
- Strong competency in Python, familiarity with PyTorch, and experience with modern software engineering best practices.
- Excellent communication skills, sufficient biomedical domain knowledge to interact effectively with diverse scientific teams.
- Enjoys a fast-paced environment and excels at executing across multiple projects.
- Familiarity with the current state-of-the-art in ML-driven protein engineering
- Nice-to-haves include experience with de novo design, NGS data, Bayesian optimization, familiarity with antibody biology and drug development, and experience training and deploying models on AWS.
Benefits
- Range of health insurance plan options through Anthem and Kaiser (monthly credit if benefit waived)
- Dental, and vision coverage through Guardian
- Additional well-being benefits through Nayya, OneMedical, Wagmo, Rula, and more
- 401(k) with company match
- DTO, two weeks of company-wide shutdown, and 12 company holidays
- Paid parental leave
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard skills
machine learningPythonPyTorchde novo designBayesian optimizationprotein sequence optimizationML methods developmentdata sciencehigh-throughput wet labdrug development
Soft skills
communication skillsmentorshipcollaborationproblem-solvingproject managementinterdisciplinary teamworkadaptabilitystrategic thinkingtechnical guidanceexecution
Certifications
PhD in ML/CSpublications in ML conferencespublications in leading journals
