Software Engineer I/II, Machine Learning

Iambic Therapeutics, Inc•Boston, MA

2d•Hybrid

About The Position

We are seeking a talented and motivated ML Software Engineer (I/II) to join the software platform group at Iambic Therapeutics. You will build, deploy, and operate ML systems that power our drug discovery efforts. You will work with our multi-disciplinary team to make production ML lifecycle workflows (training/finetuning, evaluation, inference), streamline deployment of GPU-accelerated applications, and collaborate with ML scientists to translate models into reliable, scalable software. This position will be hybrid and located in our Boston, MA office.

Requirements

Engineer I: Minimum of 5 years of related experience with a Bachelor’s degree in a scientific field; or 3 years and a Master’s degree; or equivalent work experience.
Engineer II: 8+ years relevant experience with a bachelor’s degree in a scientific field; or PhD with 3+ years; or equivalent experience.
Strong modern Python development (packaging, type hints, testing, performance), with experience in building production services and libraries.
Hands-on with ML model lifecycle and tooling (e.g. PyTorch, Hugging Face, vLLM/Triton/ONNX Runtime); data tooling (e.g. Pandas, Arrow, S3, Parquet); and workflow orchestration (e.g. Prefect, Airflow, Luigi).
Cloud deployment experience (AWS preferred), including containerization, IaC patterns, and GPU workload considerations.
Experience in scientific domains or drug discovery is a plus; ability to collaborate with scientists and communicate across disciplines is essential.

Responsibilities

Design, implement, and maintain production-grade ML workflows (fine-tuning, batch/online inference, evaluation) with strong observability and CI/CD.
Deploy GPU-accelerated ML services and jobs using modern tooling and cloud-based orchestration.
Collaborate with ML scientists and cross-functional teams to capture requirements, scope milestones, and deliver features into user workflows and services.
Conduct code reviews and mentor peers on software engineering and MLOps best practices.