- Company Name
- PreOncology
- Job Title
- Machine Learning Engineer
- Job Description
-
**Job Title:** Machine Learning Engineer
**Role Summary:**
Develop, evaluate, and production‑grade risk‑prediction models for early cancer detection, collaborating with epidemiologists and clinicians. Own the full ML lifecycle from data preparation to model deployment, ensuring robust calibration, reliability, and compliance with clinical thresholds.
**Expectations:**
- Role tier (Junior, Mid, Senior) determined by experience.
- Deliver clean, modular Python code and pipelines that scale.
- Contribute to Agile processes, code reviews, and documentation.
- Maintain calibration and drift mitigation over model lifecycle.
**Key Responsibilities:**
- Prepare and engineer biomedical datasets, including whole‑body MRI, liquid biopsy, genomics, and lifestyle data.
- Train and tune survival and risk models, performing hyperparameter optimization and reliability assessment.
- Build testable, reusable Python libraries and pipelines with clear interfaces, unit/property tests, and lightweight documentation.
- Calibrate models, detect miscalibration, and schedule recalibration to preserve clinical thresholds.
- Validate models, produce performance reports, and present findings to stakeholders.
- Orchestrate end‑to‑end ML workflows using Nextflow (DSL2) and integrate into production pipelines.
- Participate in Agile rituals, providing and receiving constructive feedback.
**Required Skills:**
- Deep understanding of machine‑learning principles beyond library usage, including model selection, trade‑offs, and pitfalls.
- Expertise in model calibration: metrics, correction techniques, and impact on clinical decision‑making.
- Proficient in Python: typing, packaging, CI testing, vectorization, profiling, and code reuse at scale.
- Experience with biological/medical datasets, ideally in oncology.
- Familiarity with Agile collaboration and iterative development.
- Hands‑on experience with Nextflow (DSL2) for data and ML pipeline orchestration.
**Required Education & Certifications:**
- Bachelor’s or higher degree in Computer Science, Data Science, Statistics, Bioinformatics, or related field.
- (Optional) Advanced degrees or certifications in machine‑learning, data science, or biomedical informatics are preferred.