SandboxAQ
Machine Learning Scientist, Bioinformatics
Job Summary
The role involves developing and applying deep learning models to predict genomic and transcriptomic profiles, particularly in the context of drug discovery. Candidates should have a Ph.D. in computational biology, bioinformatics, or related fields, with at least two years of industry experience working on deep learning for biopharma. The position requires proficiency in software tools like Python, R, and deep learning frameworks such as PyTorch, as well as experience with large-scale GPU workloads and data curation. Applicants will collaborate with cross-functional teams, contribute to research publications, and participate in international conferences to advance AI-driven drug discovery.
Required Skills
Benefits
Job Description
About SandboxAQ
SandboxAQ is a high-growth company delivering AI solutions that address some of the world's greatest challenges. The company’s Large Quantitative Models (LQMs) power advances in life sciences, financial services, navigation, cybersecurity, and other sectors.
We are a global team that is tech-focused and includes experts in AI, chemistry, cybersecurity, physics, mathematics, medicine, engineering, and other specialties. The company emerged from Alphabet Inc. as an independent, growth capital-backed company in 2022, funded by leading investors and supported by a braintrust of industry leaders.
At SandboxAQ, we’ve cultivated an environment that encourages creativity, collaboration, and impact. By investing deeply in our people, we’re building a thriving, global workforce poised to tackle the world's epic challenges. Join us to advance your career in pursuit of an inspiring mission, in a community of like-minded people who value entrepreneurialism, ownership, and transformative impact.
About the Role
We are looking for a Bioinformatician with deep experience in the training and application of deep learning models, especially but not limited to genomics, transcriptomics, spatial transcriptomics, and related fields. As a member of the Large Quantitative Model (LQM) team, you will develop completely new computational tools to reshape drug discovery.
What You’ll Do
- Develop and apply deep learned models to predict genomic and transcriptomic profiles, including after perturbation by drug molecules.
- Drive curation and use of high-quality datasets, such as single-cell RNA-seq datasets.
- Work with a cross-functional team of experts to computerize drug discovery.
- Write patents, research papers and technical documents. Participate and present at international conferences.
- Reshape drug discovery, advance machine learning, and protect humanity from disease.
About You
- Ph.D. in a field setting you up to work on deep learning models for genomics, transcriptomics, and biological pathway modelling: computational biology, bioinformatics, computer science, applied math, etc.
- 2+ years working on deep learning for biopharma in an industry context.
- Experience in training, applying, and optimizing contemporary deep learning models, including generative models, as demonstrated by:
- Experience applying deep learning models to problems in biopharma: genomics, transcriptomics, spatial transcriptomics, and/or related fields such as structural biology.
- Software skills: advanced proficiency with Python, with related software ecosystem tools (i.e. Git, Docker, Kubernetes, etc), and contemporary deep learning and informatics terms (i.e. R, Pytorch, etc)
- Excellent communication skills
Nice-to-haves
- Relevant postdoctoral training
- Experience in long-context sequence modeling
- Direct experience in drug discovery or development
- Experience running deep learning workloads on GPU clusters at large scale
- Experience working on cloud
- Contributions to open source repositories
The US base salary range for this full-time position is expected to be $192k - $269k per year. Our salary ranges are determined by role and level. Within the range, individual pay is determined by factors including job-related skills, experience, and relevant education or training. This role may be eligible for annual discretionary bonuses and equity.
SandboxAQ welcomes all.
SandboxAQ
SandboxAQ leverages the compound effects of AI and advanced computing to address some of the biggest challenges impacting society. SandboxAQ technologies include AI simulation, cryptography management for cybersecurity, and AI sensing for global organizations.
See more jobsSafe Remote Job Search Tips
Verify Employer Thoroughly
Research the company's identity thoroughly before applying. Check for a professional website with contacts, active social media, and LinkedIn profiles. Verify details across platforms and look for reviews on Glassdoor or Trustpilot to confirm legitimacy.
Never Pay to Get a Job
Legitimate employers never require payment for applications, training, background checks, or equipment. Always reject upfront payment requests or demands for bank details, even if they claim it's for purchasing necessary work gear on your behalf.
Safeguard Your Personal Information
Protect sensitive data like SSN, bank details, or ID copies. Share this only after accepting a formal, written job offer. Ensure it's submitted via a secure company system or portal, never through insecure channels like standard email attachments.
Scrutinize Communication & Interviews
Watch for communication red flags: poor grammar, generic emails (@gmail), vague details, or undue pressure. Be highly suspicious of interviews held only via text or chat apps; legitimate companies typically use video or phone calls.
Beware of Unrealistic Offers
If an offer's salary or benefits seem unrealistically high for the work involved, be cautious. Research standard pay for similar roles. Offers that appear 'too good to be true' are often scams designed to lure you into providing information or payment.
Insist on a Formal Contract
Always secure and review a formal, written job offer or employment contract before starting work or sharing final personal details. Ensure it clearly defines your role, compensation, key terms, and conditions to avoid misunderstandings or scams.