Software Engineer II, Machine Learning Systems & Productization

Iambic Therapeutics is seeking a Software Engineer to join the NeuralPLexer team, focusing on the engineering systems that enable machine learning research to translate into robust, scalable workflows for drug discovery. This role emphasizes co-development alongside ML scientists, helping design and implement workflows for model training and evaluation.

Responsibilities:

• Work embedded with ML scientists to co-develop and refine model training and evaluation workflows
• Translate experimental research code into maintainable, well-structured, and reusable systems
• Build and expand benchmarking systems for running models on structural and affinity datasets, computing metrics, and supporting reproducible evaluation
• Enable rapid iteration by developing tooling and interfaces that expose new capabilities to researchers
• Contribute to the ongoing development and productization of NeuralPLexer
• Collaborate with platform and infrastructure engineers on scaling workflows where needed, without owning core infrastructure
• Perform code reviews and actively mentor best practices in software engineering across the team
• Improve reliability, clarity, and reproducibility of ML workflows and supporting systems
• Communicate technical work effectively across a cross-functional research and engineering team

Requirements:

• 8+ years of software engineering experience (or equivalent), ideally in ML-adjacent or data-intensive environments
• Strong Python skills and demonstrated rigor in software engineering practices (testing, versioning, code quality)
• Experience working closely with ML practitioners or in research-driven environments
• Experience building or supporting ML workflows, data pipelines, or evaluation systems
• Ability to operate in partially defined, research-heavy environments and bring structure to evolving codebases
• Strong collaboration skills and comfort with pair programming and iterative development
• Experience with scientific or computational research environments
• Familiarity with structural biology, chemistry, or molecular modeling workflows
• Exposure to cloud-based systems (e.g., AWS, Kubernetes) and/or HPC
• Experience working with large-scale or heterogeneous datasets