ML Research Engineer, AI for Life Sciences
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Job Description
ML 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.
Responsibilities:
- 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:
- 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:
- 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
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