Location: SF Bay Area ( 4+ days in office )

Experience Level: 3–5+ years

Stack: Python, PyTorch, ML, LLMs, Django

Type: Full-time

🧠 About Coris

Coris is building the AI-first trust layer for global commerce. We partner with leading platforms, marketplaces, payment providers, and banks to transform how small business onboarding, monitoring, and lifecycle decisions are made - using AI on the ground to drive faster, smarter actions with less friction.

One of our customers described us as Cursor + Lovable for risk teams: flagging bad actors, assisting in investigations, and autonomously acting to mitigate fraud losses in real time.

Backed by top-tier investors and founded by experts in the payments domain, Coris is reimagining how risk gets done - not with legacy rule engines, but with domain-specific AI that thinks like your best risk analyst at scale. We help customers scale their expertise, move faster, and unlock growth - without compromising safety.

🚀 Why this role matters

Fraud detection and Risk mitigation is a uniquely hard ML problem:

• Adaptive adversaries - fraudsters continuously evolve tactics, so models must adapt faster than static rules.
• Data sparsity and imbalance - only a tiny fraction of transactions are fraudulent, but they cost millions.
• Latency and scale - decisions need to happen in tens of milliseconds at hundreds of millions of events per month, without ballooning infra costs.
  
  

This role is for someone who wants to optimize language models for fraud/risk contexts and build the backend infra that productionizes them at scale.

🥷 What you’ll do

AI/ML (~50%)

• 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.
  
  

Backend (~50%)

• 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.
• Build/Operate/Enhance data ingestion pipelines from Stripe, Adyen, and other payment processors, handling near real-time volume.
• Ensure observability with logs, metrics, and drift detection to catch when fraud tactics change.
  
  

✅ You may be a fit if you have

• 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.
• An experimental but practical mindset: ship fast, measure rigorously, iterate.
  
  

🙏 Nice to have

• 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.
  
  

🚀 Success in 3-6 months

• A distilled/quantized fraud model running in prod with 2-3x lower latency/cost than baseline, catching more fraud with fewer false positives.
• A robust pipeline for fine-tuning/evaluating fraud models that the team trusts.
• Django services powering real-time fraud scoring APIs integrated with Stripe/Adyen data flows.
  
  

🤝 How we work

• Bias toward action, measurable impact, and staying ahead of adversaries.
• Everyone owns their code in prod - from training to inference to APIs.
• Fast iterations with real customer feedback; clear metrics drive decisions.
• In-person culture with at least 4 days a week in our Palo Alto Office.
• Like any other high growth startup, we go much beyond the usual 40/50 hrs per week.
• We need high energy, high agency individuals who go the extra mile to get things done.
  
  

💰 Compensation

Competitive salary + equity + benefits.