Software Engineer II, Machine Learning Systems & Productization
I
Iambic Therapeutics, IncLife Science, Technology
Remote - US, with a preference for candidates on the East CoastFull-TimeMiddle
Salary not disclosed
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Job Details
- Experience
- 8+ years
- Required Skills
- AWSPythonKubernetes
Requirements
- 8+ years of software engineering experience
- Strong Python skills
- 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 (preferred)
- Familiarity with structural biology, chemistry, or molecular modeling workflows (preferred)
- Exposure to cloud-based systems (e.g., AWS, Kubernetes) and/or HPC (preferred)
- Experience working with large-scale or heterogeneous datasets (preferred)
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
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