Profluent
Machine Learning Scientist, BioML
About this role
Profluent is seeking a Machine Learning Scientist to innovate in protein design by leveraging advanced AI techniques. This role involves designing generative models and collaborating with teams to enhance protein-related research and applications in biomedicine.
What you'll do
- Develop predictive and generative models using experimental data
- Utilize large datasets for protein understanding and design
- Curate datasets and evaluate generative models
- Collaborate with machine learning and protein design teams
- Analyze computational approaches and share findings
- Shape scientific and strategic vision within a collaborative team
What they're looking for
- PhD or equivalent in related field
- Experience with novel machine learning techniques
- Strong publication record in major conferences or journals
- Proficiency in deep learning frameworks like Pytorch or Jax
- Familiarity with protein and nucleic acid biology
- [Experience with cloud platforms]
- [Data extraction skills]
- [Knowledge of wet lab assays]
Benefits
- High-growth career opportunity with impact
- Competitive salary with equity options
- 401(k) with employer match
- Comprehensive health benefits
- Generous PTO and work-life balance
- Professional development in AI and biology
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Profluent
Profluent develops AI-powered protein design and biomolecular engineering solutions, leveraging machine learning and generative modeling to advance biomedical applications. The company is hiring Machine Learning Scientists specializing in reinforcement learning and generative models, as well as Sustaining Engineers to maintain and enhance their automated high-throughput operations infrastructure.
View all jobs at ProfluentLikely interview questions
- Walk us through a novel machine learning model you've developed at the intersection of biology and AI. What was the biological problem, your approach, and how did you evaluate success?
- Describe your experience with generative models and representation learning. How have you applied these techniques to biological or molecular data?