Coris
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About Coris Coris is building foundational AI infrastructure for merchant risk and operations. We help software platforms, fintechs, and payment providers onboard and monitor merchants more efficiently — turning what used to take days into workflows that run in seconds. Our unified platform combines rich data and AI agents to automate risk decisions at scale. We’re growing quickly, with strong customer pull and adoption globally. The category is being defined in real time, and the opportunity in front of us is massive. If you're excited by speed, ownership, and building at the frontier of AI and risk infrastructure, we'd love to talk. We’re backed by Y Combinator, Lux Capital, Pathlight Ventures, and other top investors.
About The Role Fraud detection and risk mitigation is a uniquely hard ML problem: adaptive adversaries, data sparsity and imbalance, latency and scale. 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. The position is based in Palo Alto, CA, with a hybrid model (3 days/week in the office).
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 lightweight fine‑tuning (LoRA/PEFT), quantization, and modern serving frameworks (e.g., vLLM, TensorRT).
Build training/ evaluation pipelines that balance recall and precision.
Create golden datasets, adversarial test sets, and online/offline evaluation harnesses that mirror‑world fraud evolution.
Build feature engineering pipelines extracting various signals including 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, and 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 Might Be a Fit If You
Have 3+ years building production systems in Python/Django with Postgres.
Have hands‑on experience fine‑tuning and optimizing LLMs/SLMs, ideally in fraud, anomaly detection, or adversarial domains.
Have a track record of reducing latency/cost in ML inference without compromising accuracy.
Are comfortable working across the stack – from PyTorch profiling to Django APIs.
Have 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.
Seniority Level Mid‑Senior level
Employment Type Full‑time
Job Function Engineering and Information Technology
Industries Technology, Information and Internet
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About Coris Coris is building foundational AI infrastructure for merchant risk and operations. We help software platforms, fintechs, and payment providers onboard and monitor merchants more efficiently — turning what used to take days into workflows that run in seconds. Our unified platform combines rich data and AI agents to automate risk decisions at scale. We’re growing quickly, with strong customer pull and adoption globally. The category is being defined in real time, and the opportunity in front of us is massive. If you're excited by speed, ownership, and building at the frontier of AI and risk infrastructure, we'd love to talk. We’re backed by Y Combinator, Lux Capital, Pathlight Ventures, and other top investors.
About The Role Fraud detection and risk mitigation is a uniquely hard ML problem: adaptive adversaries, data sparsity and imbalance, latency and scale. 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. The position is based in Palo Alto, CA, with a hybrid model (3 days/week in the office).
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 lightweight fine‑tuning (LoRA/PEFT), quantization, and modern serving frameworks (e.g., vLLM, TensorRT).
Build training/ evaluation pipelines that balance recall and precision.
Create golden datasets, adversarial test sets, and online/offline evaluation harnesses that mirror‑world fraud evolution.
Build feature engineering pipelines extracting various signals including 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, and 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 Might Be a Fit If You
Have 3+ years building production systems in Python/Django with Postgres.
Have hands‑on experience fine‑tuning and optimizing LLMs/SLMs, ideally in fraud, anomaly detection, or adversarial domains.
Have a track record of reducing latency/cost in ML inference without compromising accuracy.
Are comfortable working across the stack – from PyTorch profiling to Django APIs.
Have 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.
Seniority Level Mid‑Senior level
Employment Type Full‑time
Job Function Engineering and Information Technology
Industries Technology, Information and Internet
#J-18808-Ljbffr