Staff Machine Learning Engineer

Job Summary

The Staff Machine Learning Engineer owns the end-to-end training pipeline for clinical foundation models, managing data ingestion, distributed pretraining, fine-tuning, and production deployment. This engineer builds RLHF workflows, architecturally scales clinical NLP and computer vision systems, defines evaluation benchmarks alongside clinical leads, manages distributed compute architectures (PyTorch/JAX), and mentors mid-to-senior ML engineers across the organization.

Key Roles & Operational Responsibilities

1. Foundation Model Pipeline & Distributed Training

• Pipeline Ownership: Design, build, and maintain the end-to-end training architecture for clinical foundation models, guiding assets from raw ingestion through to low-latency inference deployment.
• Large-Scale Workflows: Implement distributed pretraining, instruction fine-tuning, and Reinforcement Learning from Human Feedback (RLHF) pipelines operating on 4B+ de-identified clinical records.
• Distributed Compute: Architect high-throughput distributed training frameworks utilizing Model Parallelism, Data Parallelism, Pipeline Parallelism, and Mixed-Precision optimization routines.

2. Architecture Innovation & Interdisciplinary Alignment

• Architectural Leadership: Drive technical choices for next-generation clinical NLP models, EHR sequence representations, and medical computer vision architectures.
• Benchmark Co-Design: Partner with Clinical Informatics Leads to define rigorous model evaluation benchmarks, safety validation datasets, and clinical efficacy metrics.
• Product Translation: Translate cutting-edge research advances in applied AI into production features that drive measurable, real-world patient outcomes.

3. Technical Mentorship & Engineering Excellence

• Senior Mentorship: Guide, mentor, and elevate senior and mid-level machine learning engineers, setting org-wide standards for clean code, experiment tracking, and model monitoring.
• Best Practices: Establish modern ML engineering habits, including automated CI/CD for model weights, reproducible training runs, and privacy-preserving data handling.

Foundational Capabilities & Prerequisites

Required Technical Profile & Experience

• Academic Foundation: Master’s degree (MS) or Doctorate (PhD) in Computer Science, Machine Learning, Artificial Intelligence, or a closely related quantitative engineering discipline (or equivalent practical research experience).
• Industry Experience Baseline: Minimum of 8+ years of professional ML engineering experience, including at least 3+ years operating at the Staff or Principal Engineer level.
• Framework Mastery: Deep, hands-on expertise in large-scale deep learning frameworks (PyTorch, JAX, or equivalent C++/Python ecosystems).
• Production Track Record: Verifiable history of shipping foundation models, LLMs, or multi-node machine learning systems directly into high-throughput production environments.
• Distributed Compute Edge: Advanced command of distributed GPU training paradigms, mixed-precision training (FP16/BF16), and tensor/pipeline model parallelism techniques.
• Communication Command: Excellent written and verbal communication skills required for technical leadership and cross-functional collaboration.

Desired Skills

• Direct experience working with clinical NLP, Electronic Health Record (EHR) data structures, or HL7/FHIR interoperability standards.
• A track record of peer-reviewed publications at top ML conferences (e.g., NeurIPS, ICML, ICLR, ACL, EMNLP).
• Hands-on experience architecting models inside HIPAA-compliant or highly regulated health data environments.
• Practical exposure to privacy-preserving machine learning frameworks, differential privacy, or federated learning systems.

What Curely AI Offers

• High-Impact Machine Learning: Shape the intelligence layer powering next-generation medical AI across 4B+ records.
• Equity & Growth: Access a highly competitive salary package accompanied by an equity stake in a fast-scaling AI organization.
• World-Class Team: Collaborate closely with elite ML researchers, systems engineers, and practicing clinical informaticists.

Skills Required:

• Computer / Software / It / Data