RUST Engineer

Voto Consulting LLC; Seattle (WA); On-site; USD 140,000 - 210,000; Full time

Job summary

Voto Consulting LLC in Seattle is seeking a senior backend engineer to own end-to-end development of production Rust services, from design through deployment and operations.

You will optimize performance, memory footprint, and latency, while migrating or reworking services to preserve API compatibility and improve efficiency. Deep knowledge of Rust and JVM-based runtimes is required, with experience in cloud stacks and async runtimes.

Qualifications

• Bachelor's degree in Computer Science, Engineering, or equivalent technical field.
• 5-7+ years building and operating backend services in large-scale production environments with end-to-end ownership.
• Proficient in Rust with ownership/borrowing, lifetimes, traits, error handling, and Tokio or similar async runtimes.
• Experience in performance profiling, memory optimization, and reducing runtime overhead in production services.
• Experience migrating or rewriting services across languages while preserving API/wire-format compatibility.
• Familiarity with Smithy Rust Server SDK, ECS Fargate, CargoBrazil, and CDK with @amzn/pipelines.
• Hands-on work with AI coding agents in development workflows and multi-agent orchestration.
• Strong Java/JVM experience and understanding of GC, heap/metaspace, and JIT.

Skills

Rust

Backend development

Performance optimization

Memory optimization

Java/JVM

System design

Interoperability (FFI)

AWS / Cloud tooling

Asynchronous Rust (Tokio)

Code review

Education

Bachelor's degree in Computer Science, Engineering, or equivalent technical field

Job description

Bachelor's degree in Computer Science, Engineering, or equivalent technical field.

5-7+ years building and operating backend services in large-scale production environments, with demonstrable end-to-end ownership across design, implementation, testing, deployment, and operations.

End-to-end experience building, deploying, and maintaining production Rust applications: solid command of ownership/borrowing, lifetimes, traits, error handling, and an async runtime (e.g., Tokio); able to write, review, and debug Rust, not just read it.

Hands-on performance and memory optimization experience in production services: profiling, benchmarking, and reducing memory footprint, with a working understanding of how memory optimization is achieved (e.g., allocation patterns, data layout, eliminating runtime overhead).

Demonstrated experience migrating or rewriting services across languages or runtimes while preserving API/wire-format compatibility and proving functional equivalence.

Familiarity with the Rust service target stack or close equivalents: Smithy Rust Server SDK, ECS Fargate (ARM64/Graviton), CargoBrazil, and CDK with @amzn/pipelines.

Proficiency working hands-on with AI coding agents as a primary development workflow: driving multi-step/multi-agent workflows, structuring work within human-defined boundaries, reviewing agent output critically, and knowing when to intervene versus delegate.

Strong Java/JVM experience (or equivalent in another source language), including the runtime cost model (GC, heap/metaspace, JIT), sufficient to judge migration candidates and faithfully re-express existing service logic and framework behavior (e.g., Coral, Spring, Guice).

Preferred Qualifications

Measurable DRAM/latency/throughput improvements achieved in production distributed systems through rewrites, memory optimization, or runtime changes.

Experience with oracle/differential testing and shadow deployment (traffic mirroring, response diffing, statistical equivalence) as a validation and cutover strategy.

Experience contributing to AI agent frameworks or automated engineering workflows: not just using them, including skills, steering, or CI enforcement to prevent convention drift.

Experience with Rust interop/FFI (JNI/bindgen) and incremental, per-API migration where Rust and Java services coexist behind stable boundaries.

Experience with Amazon internal validation and operational tooling (profilers, stress-testing frameworks, CloudWatch, X-Ray) and standing up observability for a new runtime.

Strong written and verbal communication: able to present equivalence and performance results and frame proposals around fleet cost / IMR / DRAM reduction to service teams and leadership.