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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Machine Learning Scientist - Clinical Prediction - **Company:** Iambic Therapeutics, Inc - **Location:** San Diego, CA, United States (Remote available) - **Experience:** Experienced - **Salary:** $148,000.0 - $186,000.0 - **Contract:** Permanent contract - **Skills:** Clean Code Principles, Nvidia CUDA, Data Mining, Python (Programming Language), Machine Learning, Performance Tuning, Software Deployment, Software Engineering, Data Ingestion, Deep Learning, Kubernetes, Machine Learning Operations, Code Restructuring, Docker - **Published:** June 20, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=a33ac1dc7b003a1b ## About the Role Do you have experience in Testing and evaluation?, * MS in chemistry, bio/chemical engineering, or a computational STEM field with 3+ years of relevant industry or research experience, or PhD or equivalent industry experience demonstrating comparable depth * Strong Python experience, including implementing and fine-tuning deep learning models * Demonstrated experience in clinical science or working with clinical datasets * Excellent Data Science skills (problem framing, data sourcing, extraction, cleaning, visualization, EDA, modeling, tuning, storytelling, etc.) * Enough independence to own a workstream from data ingestion through evaluation * Strong engineering habits: reproducible experimentation, appropriate control strategy, clean code, testing * Comfort working with modern ML infrastructure (e.g., Docker, CUDA, Kubernetes, experiment tracking such as Weights & Biases) PREFERRED QUALIFICATIONS * Experience building and deploying clinically relevant prediction models * Familiarity with ClinicalTrials.gov/AACT data * Experience with MedDRA, pharmacovigilance, or adverse event data * Direct exposure to multi-task learning * Hands-on experience with agentic data extraction * HPC or large-scale computing experience ## Description We are seeking a Machine Learning Scientist to join the Enchant team at Iambic Therapeutics. In this role, you will design and implement clinical fine-tuning of Enchant, our multimodal transformer model trained on a wide variety of biomedical data, pushing the boundaries of what large-scale foundation models can achieve in drug discovery. This role spans data sourcing through to production deployment. You will identify, curate, and evaluate datasets that support prediction of relevant clinical endpoints (patient- and trial-level outcome modeling, safety/toxicity prediction, and PK/PD response modeling) and fine-tune Enchant to deliver critical clinical insights. This includes developing rigorous, leakage-resistant experimental frameworks, optimizing training, orchestrating runs at scale, and working with colleagues across ML and clinical functions to put these models into the hands of scientists making real therapeutic decisions., * Fine-tune large-scale multimodal transformer models for clinical and biomedical applications * Identify, characterize, and utilize datasets that can deliver insights into pharmacokinetics (PK), pharmacodynamics (PD), toxicity, clinical adverse events, and clinical trial outcomes * Develop and apply rigorous experimental approaches that account for multiple sources of potential leakage (split, metadata, trial-family, temporal, ontological, arm-comparator, etc.) * Design and maintain benchmarking and evaluation frameworks that track model quality across models and tasks * Build models with appropriate calibration, uncertainty quantification, and clinically meaningful evaluation metrics. * Collaborate with ML and software engineering colleagues to deploy and operationalize models * Partner with clinical scientists and pharmacologists to ensure model development is grounded in drug discovery and development needs * Communicate results to internal teams, external partners, and at conferences * Generate high-quality research and engineering code: refactor, test, document, and package ML components to support team velocity ## Related Videos - [Geometric deep learning for drug discovery](https://www.wearedevelopers.com/videos/264-geometric-deep-learning-for-drug-discovery) - [Coffee with Developers - Stephen Jones - NVIDIA](https://www.wearedevelopers.com/videos/1303-coffee-with-developers-stephen-jones-nvidia) - [Understanding Kubernetes in a visual way](https://www.wearedevelopers.com/videos/100085-understanding-kubernetes-in-a-visual-way) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [A Deep Dive on How To Leverage the NVIDIA GB200 for Ultra-Fast Training and Inference on Kubernetes](https://www.wearedevelopers.com/videos/1625-a-deep-dive-on-how-to-leverage-the-nvidia-gb200-for-ultra-fast-training-and-inference-on-kubernetes) - [Microservices: how to get started with Spring Boot and Kubernetes](https://www.wearedevelopers.com/videos/242-microservices-how-to-get-started-with-spring-boot-and-kubernetes) ## Related Articles - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [How to start an AI project for a good cause and boost your career](https://www.wearedevelopers.com/magazine/15-how-to-start-an-ai-project-for-a-good-cause-and-boost-your-career)