Coris is building the AI-first trust layer for global commerce to transform how small business onboarding, monitoring, and lifecycle decisions are made using AI to drive faster, smarter actions with less friction. Fraud detection and Risk mitigation is a uniquely hard ML problem due to adaptive adversaries, data sparsity and imbalance, and latency and scale requirements.
Requirements
- 3+ years building production systems in Python/Django with Postgres.
- Hands-on experience fine-tuning and optimizing LLMs/SLMs, ideally in fraud, anomaly detection, or adversarial domains.
- A track record of reducing latency/cost in ML inference without compromising accuracy.
- Comfort working across the stack - from PyTorch profiling to Django APIs.
- Prior work with imbalanced datasets (e.g., 1 in 10,000 fraud cases).
- Knowledge of feature stores, online learning, and temporal aggregation for fraud models.
- Familiarity with regulatory requirements around PII, KYC/AML, and compliance in financial data.
Responsibilities
- Fine-tune, distill, and quantize LLMs and small language models (SLMs) for fraud detection tasks: entity resolution, anomaly detection, customer communication classification, synthetic data generation.
- Optimize inference so our models run fast and cost-efficiently in production - using techniques like lightweight fine-tuning (LoRA/PEFT), quantization to smaller precisions, and modern serving frameworks (e.g. vLLM, TensorRT)
- Build training/eval pipelines for fraud models that balance recall (catch fraud) with precision (minimize false positives).
- Create golden datasets, adversarial test sets, and online/offline evaluation harnesses that mirror real-world fraud evolution.
- Build feature engineering pipelines extracting various signals including the non-obvious latent ones.
- Architect and own Python/Django services that integrate model predictions directly into customer-facing APIs.
- Model complex fraud/risk data in Postgres; ensure queries and aggregations scale to billions of records.
Other
- 3-5+ years experience
- 4+ days in office
- An experimental but practical mindset: ship fast, measure rigorously, iterate.
- Bias toward action, measurable impact, and staying ahead of adversaries.
- High energy, high agency individuals who go the extra mile to get things done.
