Generative ML Scientist - Protein Design

Latent Labs; San Francisco (CA); Hybrid; USD 180,000 - 240,000; Full time

Benefits offered by this job

Private health insurance

Pension/401(K) contributions

Generous leave policies

Hybrid working

Travel opportunities

Job summary

A cutting-edge synthetic biology firm in San Francisco is seeking a highly skilled machine learning researcher specializing in generative modeling. Ideal candidates will have a strong background in ML with a proven track record in leading projects and contributions to notable publications. Responsibilities include building ML models for real-world applications, collaborating within interdisciplinary teams, and ensuring high coding standards. Competitive compensation includes benefits such as private health insurance and a hybrid work model.

Qualifications

• Significant experience in generative modeling with notable machine learning projects.
• Proficient in writing robust, maintainable ML code.
• Experience with ML data pipelines for training and evaluation.
• Strong understanding of optimizing ML models for performance.

Responsibilities

• Build machine learning models that function in real-world scenarios.
• Collaborate with interdisciplinary teams to curate training data.
• Prototype ML models based on lead metrics and connectivity.
• Maintain high coding standards in collaborative codebases.

Skills

Generative modeling

Machine learning coding

ML data pipelines

Model optimization

Statistical analysis

Job description

A cutting-edge synthetic biology firm in San Francisco is seeking a highly skilled machine learning researcher specializing in generative modeling. Ideal candidates will have a strong background in ML with a proven track record in leading projects and contributions to notable publications. Responsibilities include building ML models for real-world applications, collaborating within interdisciplinary teams, and ensuring high coding standards. Competitive compensation includes benefits such as private health insurance and a hybrid work model.