Staff ML Research Engineer (AI)

TL;DR

Staff ML Research Engineer (AI/Simulation): Architecting and optimizing large-scale ML and physics-based models to accelerate drug and materials discovery with an accent on distributed training pipelines and GPU-level performance. Focus on transitioning complex research prototypes into robust production-grade systems and scaling computational infrastructure.

Location: Remote within the United States

Salary: $177,838 – $306,000

Company

hirify.global is a high-growth company delivering AI solutions that address world-class challenges in sectors like life sciences, financial services, and cybersecurity.

What you will do

• Bring scientific paper concepts into scalable machine learning algorithms.
• Lead the benchmarking and execution of complex datasets and models.
• Implement advanced software and hardware optimizations for distributed cloud GPU environments.
• Drive software through the entire product lifecycle from research to production launch.
• Collaborate across interdisciplinary teams to bridge research code with commercial product reality.

Requirements

• MSc or PhD in Computer Science, Physics, Chemistry, or related quantitative field.
• 5+ years of industry experience in professional software development.
• Proven experience training and optimizing large-scale ML pipelines on distributed cloud GPUs.
• Proficiency with modern frameworks such as PyTorch or TensorFlow.
• Experience supporting models in external-facing products and bridging research with production.
• Must be based in the United States for this remote role.

Nice to have

• Direct experience in biopharma, affinity modeling, or generative chemistry.
• Proven history of launching successful commercial software products.
• Familiarity with MLOps practices on major cloud platforms.

Culture & Benefits

• Comprehensive medical, dental, and vision coverage for employees and dependents.
• Retirement savings with company matching and equity participation.
• Flexible paid time off and company-wide seasonal breaks.
• Support for sustainable performance with flexible work arrangements.
• Opportunities for continuous learning through cross-functional collaboration and development programs.