Machine Learning Researcher | Engineer — Seeking Exceptional, Top-Tier Talent
Why This Role Stands Out
This hybrid role offers an exceptional opportunity to pioneer novel machine learning approaches at the forefront of AI, molecular simulation, and biophysics, developing cutting-edge solutions for complex scientific challenges. If you are a top-tier expert driven by innovation and eager to collaborate with leading researchers, you will thrive in this environment focused on groundbreaking discovery. This is your chance to make a significant impact and advance your career in a dynamic and intellectually stimulating setting.
Quick Overview
Job Description
Location: New York City, NY | Hybrid
Machine Learning Researcher / Engineer — Seeking Exceptional, Top-Tier Talent
The Opportunity
StaffRight Associates is recruiting to identify an Exceptional, Top-Tier Talent who is an ''EXPERT'' as a Machine Learning Researcher and Engineer for a premier interdisciplinary research organization in New York City.
This is an opportunity for an individual operating at the highest level of machine learning, deep learning, and computational research to work on problems at the intersection of AI, molecular simulation, biophysics, chemistry, biology, and high-performance computing.
The work is not about applying established technology to routine problems. It is about developing new approaches to difficult scientific problems where conventional methods are often insufficient.
What You Will Do
- Develop and investigate advanced machine-learning and deep-learning methods for scientific applications.
- Design, train, and optimize sophisticated neural-network and computational models.
- Apply ML to areas including molecular simulation, quantum chemistry, biomolecular systems, and drug discovery.
- Develop research ideas from hypothesis through implementation, experimentation, and validation.
- Work directly with exceptional computer scientists, ML researchers, chemists, and biologists.
- Build high-performance computational solutions capable of supporting demanding scientific research.
What We Look For
We are seeking proven technical excellence—not keyword coverage.
Strong candidates will demonstrate:
- Deep expertise in machine learning and deep learning.
- Exceptional Python programming ability.
- Strong mathematical, algorithmic, and computational foundations.
- Evidence of original research, innovation, or significant technical achievement.
- The ability to attack ambiguous, difficult problems independently.
- Intellectual curiosity and the ability to work across scientific disciplines.
Relevant experience may include generative modeling, representation learning, graph or geometric learning, reinforcement learning, scientific ML, optimization, probabilistic modeling, or related areas.
Experience in molecular dynamics, structural biology, chemistry, biophysics, or drug discovery is highly valuable..!
A Masters, or Ph.D. in Computer Science, Mathematics, Physics, Engineering, Statistics, or another rigorous STEM discipline is required.
Why This Opportunity
This is for someone who wants to work on problems that are genuinely difficult and genuinely interesting.
You will work alongside highly accomplished researchers and engineers in an environment where technical depth, originality, intellectual curiosity, and measurable results matter more than titles or hierarchy.
If you have developed something exceptional, published important research, created novel ML techniques, or solved problems that others could not, you may be who is needed.
Compensation & Benefits
$300,000–$900,000 annual base salary, plus:
- Sign-on and year-end bonuses
- Generous benefits
- Relocation assistance
Hybrid: 3 Day in office, 2 day remote
Confidential Representation
StaffRight Associates always provides confidential representation, direct advocacy, technical positioning, and interview guidance throughout the process.
If your work demonstrates exceptional ability in machine learning and you are looking for problems worthy of your capabilities, we would like to speak with you.
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