Job Opportunities API

FTMO is launching a new crypto-native product. By design, it operates without traditional identity verification. This creates a fundamentally different security landscape: identities are cheap to create, coordination is easy to hide, and adversarial actors will find creative ways to exploit the system for profit. This role owns the entire anti-fraud and data integrity layer. You'll design the detection systems, economic safeguards, and data infrastructure that make attacks expensive and unreliable. This is equal parts data engineering, adversarial thinking, and economic design, you're not just catching bad actors after the fact, you're making the system structurally hard to exploit. To be direct: this is more of a hard problem to solve than a coding job. The challenge isn't writing pipelines, it's figuring out what the right detection approach even is, modeling whether it works economically, and then building it. You'll spend as much time thinking, researching, and modeling as you will writing code. You won't be alone. There's an engineering team building the core platform alongside you, and the anti-fraud team will grow as the product scales. But you're laying the foundations, the architecture, the tooling decisions, the detection philosophy, that everything else builds on. What you design in the first months will shape how the platform defends itself for years. WHAT WILL BE YOUR AGENDA? - Design and build a multi-layered fraud detection framework, combining on-chain data analysis, behavioral signals, statistical correlation, and economic mechanism design. - Build real-time detection pipelines that flag suspicious patterns as they happen. - Develop graph-based analysis of on-chain transaction flows, funding sources, destination clustering, shared history patterns, to identify coordinated activity across accounts. - Implement statistical correlation engines across active accounts to detect coordinated or adversarial strategies operating over time. - Design economic defense mechanisms that make attacks unprofitable by construction, this means modeling attack scenarios, running simulations, and tuning platform parameters before writing detection code. - Build a composite risk scoring system that aggregates signals from multiple detection layers into actionable decisions (monitoring, delayed processing, manual review, account restrictions). - Collect and analyze behavioral signals from the platform, frontend, session patterns, interaction fingerprints, timing analysis, as additional inputs to the scoring model. - Build internal tooling and dashboards for investigation and manual review. - Evaluate and select the tech stack for the anti-fraud domain: data pipeline infrastructure, graph storage, real-time processing, statistical analysis tooling. This is a greenfield decision, you propose, we discuss as a team. WHAT WILL YOU BRING TO THE TABLE? - 3+ years in data engineering, fraud detection, security engineering, or quantitative analysis - Strong programming skills, Python, Rust, Go, or similar. We care about your ability to build production data systems, not a specific language. - Experience with graph databases or graph analysis techniques for relationship mapping and cluster detection. - Experience building data pipelines with streaming or real-time processing (Kafka, Redis Streams, or similar). - You think like an attacker. You can look at a system, find the exploit, and then design the defense. If you've never broken something to understand it, this role will frustrate you. - Comfort with ambiguity. There's no playbook for this, you'll be researching attack vectors, prototyping detection approaches, and iterating based on what actually works. - CS degree or equivalent, we care about what you've built, not where you studied WHAT WOULD BE NICE TO HAVE? - Experience with blockchain data analysis, on-chain forensics, or wallet clustering (Chainalysis, Arkham, Nansen-style analysis).