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.
For this role, you should be located in Canada and able to work within EST/EDT OR PST hours. We are fully remote.
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
We're on a mission to simplify the everyday lives of consumers. Post-purchase is a critical phase of the customer journey. That's why we created Narvar - a platform focused on driving customer loyalty through seamless post-purchase experiences that allow retailers to retain, engage, and delight customers. If you've ever bought something online, there's a good chance you've used our platform!
From the hottest new direct-to-consumer companies to retail’s most renowned brands, Narvar works with GameStop, Neiman Marcus, Sonos, Nike, and 1300+ other brands. With hubs in San Francisco, Atlanta, London, and Bangalore, we've served over 125 million consumers worldwide across 10+ billion interactions, 38 countries, and 55 languages.
Pioneering the post-purchase movement means navigating into the unknown. Our team thrives on this sense of adventure while nurturing a mindset of innovation. We're a home for big hearts and we leave our egos at the door. We work hard but we always make time to celebrate professional wins, baby showers, birthday parties, and everything in between.
We are an equal-opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.
Below is the estimated annual salary for this position and does not include the other components that make up a Narvar offer including: annual bonus, equity, and benefits.
The range reflects the minimum and maximum target for new hire salaries for the position across Canada. Within the range, individual compensation packages are based on factors unique to each candidate, including but not limited to, skill set, education and certifications, and work location.
Narvar Salary Range
$240,000—$270,000 CAD
Please read our Privacy Policy to learn what personal information we collect in connection with your job application, and how we may use and share it.
