Rust Software Engineer Lead, Distributed Ledger

FIS Global•New York, NY

7d•$133,980 - $227,770•Hybrid

About The Position

Keystone is building bank-grade network infrastructure for regulated digital value movement. The product runs on the LYRIQ platform, which provides the ledger, integration, control, and operating services needed to support tokenized deposits, digital liquidity, and related bank-led payment and settlement use cases. The Keystone engineering team builds the ledger and network services behind regulated digital money and bank-led settlement flows. The core platform is a permissioned, BFT-replicated distributed ledger written primarily in Rust, with a signed append-only transaction log, hierarchical account trees, programmable transfer and access rules, and hardware-backed key custody. As Senior Rust Engineer, Distributed Ledger, you will work close to the metal on consensus, cryptography, state storage, and correctness engineering. You will also build and support the gRPC services, SDKs, operating evidence, and integration patterns that Keystone customers and bank engineering teams use to connect to the platform.

Requirements

Six or more years of professional backend or systems engineering, including at least three years writing production Rust.
Deep, practical async Rust: tokio, futures, trait and generic design, lifetimes, Send and Sync reasoning, and the judgement to know when a lock beats a channel.
Hands-on experience with blockchain or distributed ledger internals: a consensus protocol (BFT, Raft, Paxos, or similar), transaction validation, state commitment such as Merkle trees, block or state storage, or node-to-node networking.
Applied cryptography fundamentals: digital signatures, hashing, key derivation and custody, and a clear instinct for where the trust boundaries sit in a system.
Experience designing and evolving service APIs, ideally gRPC and Protocol Buffers, including backwards compatibility across versioned clients in the field.
Comfort operating what you build: Docker, Kubernetes and Helm, metrics-driven and trace-driven debugging, and the persistence to take a production incident down to root cause.
A testing discipline that matches the stakes: unit, integration, and simulation-level testing of concurrent and distributed code, including diagnosing and properly fixing timing-sensitive failures rather than retrying them away.
Clear technical writing; you can produce a design note that both your own team and a bank's engineers can act on.

Nice To Haves

Experience with central bank digital currency, tokenized deposits, payments, or core banking systems, and familiarity with standards such as ISO 20022.
Trusted execution environment work (Intel SGX, AWS Nitro Enclaves, vsock transports) or HSM and PKCS#11 integration.
Language, compiler, or virtual machine implementation experience: parsers, type systems, bytecode interpreters.
Columnar analytics at scale with ClickHouse, event pipelines on Kafka, or disciplined schema migration work with sqlx and Postgres.
Familiarity with a second language in our stack: TypeScript, Python, or Go.
Public contributions to Rust or distributed ledger open source projects.

Responsibilities

Consensus and the ledger core: our Pala-family BFT consensus implementation, block production and finality, state storage on RocksDB, snapshotting, and state recovery.
Cryptography and key custody: Ed25519 transaction signing, Merkle-committed state, threshold and shard-based key handling, AWS KMS and on-premise HSM integration, and trusted execution environments (Intel SGX via Fortanix, AWS Nitro Enclaves).
Programmable money: the expression language, compiler, and bytecode VM behind our role-based access control and transfer rules, including the parser, type checker, and interpreter.
Ledger APIs and SDKs: gRPC services built on tonic and Protocol Buffers, plus the REST gateways, CLI, and multi-language client SDKs that integrators depend on.
Indexing and observability: streaming ledger observations into ClickHouse and Postgres for balance queries, block explorers, and reporting, instrumented end to end with Prometheus metrics and tracing.
Correctness engineering: deterministic simulation harnesses, per-node fault injection, property-based testing with proptest, fuzzing, and formal model checking of consensus invariants.