Associate AI Scientist

Pay Rate Low: 30 | Pay Rate High: 38 Excellent opportunity to work on cutting-edge AI applications in genomics within a collaborative, research-driven biotech environment, contributing to impactful projects at the intersection of machine learning and biology. Job Title: Associate AI Scientist (Contract) Location: South San Francisco, CA (Onsite with potential hybrid flexibility) Duration: 6–10 months +benefits included Schedule: Monday–Friday, 8am-5pm Pay Rate: $30–$38/hr (flexible for strong candidates) Role Overview This position focuses on developing and applying modern machine learning approaches to DNA sequence data to better understand how genetic variants influence biological function. The work supports research efforts in genomics, regulatory biology, and variant interpretation. Key Responsibilities • Develop and evaluate machine learning models that link DNA sequence to biological function • Apply and adapt sequence-based foundation models to improve variant effect prediction • Build benchmarking and evaluation pipelines to assess model performance and robustness • Analyze and interpret the biological impact of genetic variants using computational models • Train or fine-tune models using genomics datasets (e.g., ATAC-seq, RNA-seq, ChIP-seq, MPRA) • Collaborate with computational scientists and biologists on cross-functional research projects • Present findings to internal scientific teams and contribute to reports or publications • Deliver a final project presentation to a broader internal audience Required Qualifications • Master’s student, PhD student, recent PhD graduate, or equivalent experience in a relevant scientific or computational discipline. • Familiarity with sequence modeling and genomic machine learning workflows • Experience with sequence-based or foundation models (DNA/RNA language models or similar) • Strong Python skills and experience with deep learning frameworks (PyTorch or TensorFlow) • Ability to read and apply concepts from modern ML research literature • Academic background in computational biology, computer science, biology, or related field • Foundational knowledge of gene expression and cell biology Preferred Qualifications • Experience communicating computational biology concepts to diverse audiences • Familiarity with statistical fine-mapping methods (e.g., SuSiE, FINEMAP, PAINTOR) • Experience applying ML models to biological interpretation or experimental design • Knowledge of common genomics data formats, genome annotations, and large datasets • Interest in gene regulation, enhancers, promoters, and non-coding genome biology • MUST be authorized to work in the United States without sponsorship. INDBH