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ML Research Engineer, AI for Life Sciences
SandboxAQML Research Engineer working on cutting-edge ML models for drug discovery and materials development at SandboxAQ. Join a tech-focused global team with expertise across multiple disciplines.
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in developing productionized software from research, particularly in ML applications within biopharma and structural biology. Proficient in MLOps practices and capable of integrating complex datasets into large-scale simulation frameworks.
Highest-signal resume keywords
PhD In Computer ScienceProductionized Software DevelopmentMachine Learning Model IntegrationMLOps PracticesBiopharma Experience
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Advanced Computational MethodsData Science TechniquesML Software Artifacts DevelopmentCommercial Software Product LaunchStructural Biology Knowledge
Soft Skills
Interdisciplinary CollaborationIdeation Leadership
Tools & Technologies
Cloud PlatformsSimulation Frameworks
Industry Keywords
BiopharmaAffinity ModelsStructure-PredictionGenerative Chemistry
Tech Stack
Tools & technologiesCloud
About the role
Key responsibilities & impact- Bring novel ideas and the content of scientific papers into high-performing and robust scientific code.
- Lead the ideation, benchmarking, and execution of complex datasets and ML models, ensuring seamless integration into large-scale simulation frameworks.
- Drive software through the entire product lifecycle—from foundational research and implementation to launch and long-term support—ensuring technical excellence at every stage.
Requirements
What you’ll need- PhD, or research-focused MSc, in Computer Science, Physics, Chemistry, or a related quantitative field focused on advanced computational methods.
- Staff (5+ years) industry experience developing productionized software in professional teams.
- Experience or training in data-science related tasks related to structural biology.
- Expertise translating research papers into concrete ML software artifacts.
- Experience supporting models in external-facing products, demonstrating the ability to bridge the gap between "research code" and "product code".
- Direct experience in biopharma or training leading-edge affinity, structure-prediction, or generative chemistry models.
- A history of developing and launching successful commercial software products within a professional engineering team.
- Familiarity with MLOps practices on major cloud platforms to support automated scaling and model monitoring.
- Experience working in interdisciplinary environments where AI intersects with physical or biological sciences.
Benefits
Comp & perks- Comprehensive medical, dental, and vision coverage for employees and dependents with generous employer premium contributions
- Retirement savings with company matching
- Paid parental leave
- Inclusive family-building benefits
- Flexible paid time off
- Company-wide seasonal breaks
- Support for flexible work arrangements that enable sustainable performance
- Opportunities for continuous learning and growth through on-the-job development, cross-functional collaboration, and access to internal learning and development programs