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обновлено 5 дней назад

Senior / Principal ML Biophysicist

200 000 - 270 000$
Формат работы
onsite
Тип работы
fulltime
Грейд
senior
Английский
b2
Страна
US
Вакансия из списка Hirify.GlobalВакансия из Hirify Global, списка международных tech-компаний
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Описание вакансии

Текст:
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TL;DR
Senior / Principal ML Biophysicist (Molecular Dynamics/AI): Building scalable molecular dynamics pipelines and integrating physics-based models with machine learning frameworks to accelerate biomolecular engineering and discovery with an accent on protein structure modeling, first-principles analysis, and sparse experimental data. Focus on designing predictive frameworks, encoding scientific priors and constraints, validating models against internal and public datasets, and prototyping algorithms for molecular development workflows.

Location: San Francisco Bay Area, on-site

Salary: $200,000–$270,000 per year, plus equity

Company

hirify.global is a frontier research lab developing brain-computer interfaces that combine biological systems with artificial intelligence.

What you will do

  • Build scalable pipelines for protein structure modeling, molecular dynamics, and downstream machine learning integrations.
  • Architect tools and workflows for simulating, analyzing, and interpreting biomolecular dynamics-to-function relationships.
  • Collaborate with wet-lab scientists to define optimization objectives and encode domain-specific priors and constraints.
  • Prototype, benchmark, and validate modeling frameworks using internal and public datasets.
  • Develop machine learning frameworks that incorporate first-principles scientific models.
  • Prototype novel algorithms and contribute to the long-term research roadmap and discovery workflows.

Requirements

  • Strong grounding in deep learning, protein structure modeling, and molecular dynamics.
  • Working knowledge of transfer-learning strategies.
  • Proficiency in Python, PyTorch, and JAX, with experience writing clean, reproducible, production-grade code.
  • Experience bridging machine learning and experimental science using sparse, noisy, or high-cost data.
  • Collaborative, systems-level mindset.

Nice to have

  • Familiarity with neuroscience.
  • Familiarity with language or state-space models.

Culture & Benefits

  • Interdisciplinary collaboration across molecular engineering, synthetic biology, neuroscience, physics, engineering, and data science.
  • Opportunities to work on high-bandwidth, less-invasive brain-computer interface technologies.
  • Equity included in the compensation package.
  • Equal opportunity employment and reasonable accommodations for applicants with disabilities.

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