Lead Applied Scientist – NLP and Ranking

Department/Project Description

GlobalLogic takes an opportunity to partner with a leading global threat intelligence platform to design and deliver an enterprise-scale, Human-in-the-Loop (HITL) AI agent platform. The initiative centers on accelerating expert analyst decision-making by augmenting high-complexity threat intelligence workflows with cutting-edge generative and agentic AI systems.


Job Description

Essential qualifications

• MSc or PhD in NLP, information retrieval or machine learning with publications, or equivalent industry research experience.
• 5+ years of applied ML, with models in production use.
• Deep experience fine-tuning transformer encoders with PyTorch and Hugging Face.
• Multi-label and imbalanced classification, probability calibration and threshold optimization.
• Learning from user feedback: learning-to-rank, active learning, bandit/exploration methods or counterfactual evaluation.
• Rigorous experiment design and statistical analysis.

Desirable qualifications

• ModernBERT or other long-context encoder models.
• Multi-stage ranking or recommender systems.
• LLM post-training (SFT, LoRA, DPO), reward or judge models, and reranker or embedding fine-tuning.
• Vertex AI Training and Pipelines.
• Experience in security, threat intelligence or fraud.

Job Responsibilities

Key responsibilities

• Fine-tune, evaluate and calibrate ModernBERT for multi-label alert classification and high-recall shortlisting.
• Design the two-stage triage cascade: model thresholds, the hand-off to the LLM, and how each stage is measured.
• Convert analyst feedback into training data through label cleaning, active learning and defined retraining criteria.
• Correct for selection bias through exploration sampling of rejected alerts and counterfactual evaluation.
• Design the dynamic few-shot strategy:
  how decision points are represented
  how examples are selected
  how conflicting or stale human decisions are handled.
• Define the feedback and reward schema with the Lead Agent Engineer.
• Set offline and online evaluation, and review retrained models before production promotion.