Software Engineer II, Machine Learning Systems & Productization

Iambic Therapeutics, Inc•San Diego, CA

18h•Remote

About The Position

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 sits directly alongside ML scientists and emphasizes co-development: helping design, implement, and harden the workflows used to train, evaluate, and apply models on structural (e.g., protein–ligand complexes), affinity, and synthetic data. You will play a key role in turning research code into reliable, reusable systems—without being responsible for core model development. This is a remote position in the US, with a preference for candidates on the East Coast.

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

Nice To Haves

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

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