- Company Name
- Delty (YC X25)
- Job Title
- Machine Learning Engineer
- Job Description
-
Job Title: Machine Learning Engineer
Role Summary:
Design, develop, and deploy production‑ready machine learning solutions for healthcare workflow optimization. Own the entire ML lifecycle, from feature engineering and model training to deployment, monitoring, and retraining, while collaborating with backend engineers, product leaders, and founders to embed AI services into operational systems.
Expectations:
- 3+ years of end‑to‑end machine learning production experience.
- Proven ability to build scalable data pipelines, train structured‑data models, and manage model lifecycle (versioning, monitoring, rollback).
- Strong backend engineering practices: clean code, services or batch job development, database interaction.
- Sound system‑design judgment balancing accuracy, explainability, latency, scalability, and maintainability.
- Excellent communication of modeling decisions to non‑technical stakeholders.
- High ownership, flexibility, and swift delivery in a fast‑paced startup setting.
Key Responsibilities:
1. Build and own complete ML systems for ranking, prioritization, and prediction tasks (e.g., high‑risk case identification).
2. Design robust data pipelines to transform raw healthcare data into high‑quality features for ML models.
3. Train, evaluate, and iterate on models, selecting appropriate algorithms and evaluation metrics.
4. Deploy models as production services or batch jobs, establishing clear versioning, monitoring, and rollback procedures.
5. Partner with backend and product teams to integrate ML outputs into real workflows and decision‑making platforms.
6. Make architectural decisions on model choice, retraining cadence, guardrails, and operational constraints.
7. Work directly with founders and senior engineers to translate product needs into scalable, maintainable ML solutions.
Required Skills:
- Structured data machine learning: feature engineering, regression/classification, ranking/prioritization.
- Full ML lifecycle experience: data preparation, train/test split, evaluation, deployment, retraining, monitoring.
- Backend engineering: production‑grade code, services/batch jobs, databases, data pipelines.
- System design fundamentals: trade‑offs among complexity, latency, scalability, maintainability.
- Strong problem‑solving, communication, and ownership mindset.
Required Education & Certifications:
- Bachelor's or higher in Computer Science, Statistics, Data Science, Engineering, or related field.
- Relevant certifications (e.g., AWS Certified Machine Learning, GCP Professional Machine Learning Engineer) preferred but not mandatory.
Optional/Bonus:
- Healthcare or operational decision‑support experience.
- LLM deployment (retrieval‑augmented generation, fine‑tuning, prompt engineering).
- Startup/founder experience and model monitoring/data‑drift detection expertise.
San francisco, United states
Hybrid
Junior
15-12-2025