Machine Learning Systems Lead (Identity, Fraud, and AI Intelligence) at Narvar

Narvar is growing! We're looking for a leader to own the machine learning systems behind post-purchase commerce — identity, fraud, and the intelligence layer that agentic AI runs on.

Hundreds of millions of consumers interact with Narvar every year, across 20B+ orders and 1,300+ retail brands. That data powers three systems: Graphite, our identity resolution engine that ties fragmented consumer data into a single verified identity across retailers; IRIS, our fraud and returns-abuse detection engine built on top of it.

You’ll own the ML organization behind all of it — the models, the platform they run on, and the people who build them. This is a role for someone who wants a system with real consequences: fraud decisions that move retailer margin, identity resolution that agents make automated decisions on, and an adversary on the other side who adapts every quarter.

Day-to-day

• Own the ML charter across identity resolution, fraud and abuse detection, risk scoring, and consumer intelligence — strategy, roadmap, and delivery
• Build and grow a high-performing, globally distributed team of ML engineers; hire, coach, and develop senior ICs and managers
• Set the technical bar for how models get built, evaluated, deployed, and monitored — and hold the org to it
• Push identity resolution coverage, precision, and profile classification accuracy against a measured, frozen-holdout baseline — not against vibes
• Own IRIS model performance end-to-end: detection rate, false-positive rate, label quality, and the feedback loops that keep both honest as fraud patterns shift
• Build the ML platform layer — feature stores, training pipelines, model registry, online serving, drift and performance monitoring — so model velocity isn’t bottlenecked on infrastructure
• Partner with the AI engineering team so identity and risk signals are first-class inputs to NAVI’s agent decisions
• Work directly with Product, Engineering, Security, and Customer Success to translate messy retailer problems into ML problems worth solving — and to say no to the ones that aren’t
• Own build-vs-buy and data-partner decisions (third-party identity data, enrichment providers), including the economics
• Communicate model performance, risk, and tradeoffs credibly to executives, retailers, and the board

What We’re Looking For

We care more about judgment and ownership than credentials.

You’re likely a strong fit if you:

• Have 12+ years in engineering with 5+ years managing ML or data teams, including managing managers or senior ICs
• Are an engineer at heart — you can still read a training pipeline, review a feature spec, and tell when an eval is measuring the wrong thing
• Have shipped ML systems that make consequential automated decisions in production, and have owned them after launch — drift, retraining, incidents, and all
• Have deep experience with at least one of: entity resolution / identity graphs, fraud and abuse detection, risk scoring, or anomaly detection at scale
• Understand what makes ML different from software: labels are noisy and delayed, systems fail silently, offline metrics lie, and last quarter’s model is fighting last quarter’s adversary
• Have opinions about evaluation — precision/recall tradeoffs on heavily imbalanced data, holdout hygiene, feedback loops where the model’s own decisions contaminate future labels
• Have built or scaled ML infrastructure: feature pipelines, training orchestration, online model serving with real latency budgets, monitoring and alerting
• Are fluent in Python and comfortable in a modern data stack (Spark, Airflow or equivalent, streaming, cloud data warehouses — we run on GCP)
• Have hired well, retained well, and can point to engineers whose careers got better on your watch
• Excel in a fast-paced environment with a flat structure — you set direction by earning trust, not by mandate
• Have a BS/MS in computer science, statistics, or an equivalent background

Bonus points

These aren’t hard requirements, but strong indicators:

• You’ve worked on identity resolution or graph systems against third-party consumer data (credit bureau, telecom, or similar), including the matching logic and confidence scoring underneath
• You’ve operated adversarial ML systems where attackers actively adapt to your detection
• You’ve built real-time inference paths where identity or risk has to be resolved inside a request budget
• You’ve worked in retail, payments, fintech, insurance, or marketplace trust & safety
• You’ve partnered closely with LLM/agent teams and understand how structured ML signals ground agentic decisions
• You’ve navigated privacy, consent, and data-governance constraints on consumer identity data
• You’ve done 0→1 — stood up a function, a platform, or a data partnership from nothing
• You use modern tooling (including AI-assisted development workflows) to increase leverage, not outsource thinking

Why ML at Narvar?

Because the data is genuinely rare and the problems are genuinely hard.

• The dataset is a moat. Cross-retailer visibility across 20B+ orders means we can see coordinated fraud patterns no single retailer can — a returns abuser hitting five brands looks like a good customer at each one.
• The problem is adversarial and unsolved. Returns fraud and policy abuse are growing faster than retail itself, and the counterparty adapts. There is no off-the-shelf answer.
• The stakes are measurable. Fraud loss, false-positive cost, identity coverage, resolution rate. Not vanity metrics.
• It feeds something bigger. As AI shopping agents enter commerce, every agent will need a trust layer for returns, claims, and delivery promises. Identity and risk are that layer — and you’d own it.
• The work is greenfield enough to matter. Real-time resolution, cold-start modeling, historical backfill, new data partnerships — these are decisions still being made, not legacy you inherit.

Why Narvar (from an engineer’s perspective)?

• Real scale, real customers, real consequences
• Startup-level ownership with platform-level impact
• Teams that value thinking, judgment, and responsibility
• Low ego, high trust, and room to do your best work