PR
Rust Engineer
Accepting applicationsPlatform Recruitment · San Francisco Bay Area
Full-Time Mid_senior AIFPGARTL
Posted
21h ago
Category
Verification
Experience
Mid_senior
Country
United States
We're working with a well-funded, highly selective AI infrastructure startup building silicon for large-scale AI inference and training. Founded by veteran chip architects who previously led a major AI hardware program at a top tech company, the business has grown fast and is backed by top-tier investors.
They're looking for Rust engineers to help build best-in-class silicon for high-performance, sustainable GenAI, across compute, memory management, and high-speed connectivity.
The Role
The environment here is genuinely unusual for a Rust role: a custom, code-generated cycle-accurate model, built in Rust, sits at the center of the whole design flow, feeding architecture validation, RTL validation, and post-silicon validation, with hardware description largely generated from this model rather than handwritten. You'd work on the simulator and host-side interface library that make up this model, alongside writing the targeted and randomized tests that stress it. The team is also building AI directly into the flow itself, including retrieval, knowledge-graph, and automated-reasoning tooling.
Scope isn't fixed here — they want versatile engineers who can move between core modelling, tooling, and testing rather than staying in one lane.
Responsibilities
Build and extend internal infrastructure and tooling in Rust to support hardware and software teams
Design and implement the Rust-based simulator that underpins the verification flow
Build the host-side interface library connecting software and hardware layers
Write targeted and randomized tests to systematically surface corner cases and rare event sequences
Partner across hardware, architecture and software teams to translate specifications into concrete, testable properties
Requirements
Production-level Rust experience — you've shipped real systems and are comfortable with ownership, traits, generics with bounds, and enums as sum types
3+ years of engineering experience with strong fundamentals: clean, well-tested, well-documented code, and comfort with build systems, version control and CI
Genuine interest in hardware and willingness to ramp up quickly
Remote (with travel) or hybrid, based in the Bay Area
Nice to Have
Hands-on hardware experience in any form — FPGA, embedded systems, robotics, or prior hardware-adjacent roles; accelerator experience is a strong plus
Experience with foreign function interfaces (Rust FFI ideal, any cross-language experience valuable)
Exposure to formal methods, model checking or property-based testing (e.g. proptest, Kani)
Understanding of computer architecture: pipelines, caches, memory hierarchies, on-chip interconnects
Familiarity with ML systems/frameworks
What Sets This Role Apart
Tool-building culture: the design flow is built in-house, generated from a custom modelling layer rather than relying on legacy, vendor-driven methodology
AI-native flow: active work integrating AI tooling (retrieval, knowledge graphs, automated reasoning) directly into design and verification
Real ownership: your work feeds directly into production silicon at a company scaling fast
Flexible scope: significant freedom to shape what you work on, from core modelling and tooling through to deeper architecture involvement
Package & Process
Compensation is highly competitive, with strong equity and full benefits.
Show more Show less
They're looking for Rust engineers to help build best-in-class silicon for high-performance, sustainable GenAI, across compute, memory management, and high-speed connectivity.
The Role
The environment here is genuinely unusual for a Rust role: a custom, code-generated cycle-accurate model, built in Rust, sits at the center of the whole design flow, feeding architecture validation, RTL validation, and post-silicon validation, with hardware description largely generated from this model rather than handwritten. You'd work on the simulator and host-side interface library that make up this model, alongside writing the targeted and randomized tests that stress it. The team is also building AI directly into the flow itself, including retrieval, knowledge-graph, and automated-reasoning tooling.
Scope isn't fixed here — they want versatile engineers who can move between core modelling, tooling, and testing rather than staying in one lane.
Responsibilities
Build and extend internal infrastructure and tooling in Rust to support hardware and software teams
Design and implement the Rust-based simulator that underpins the verification flow
Build the host-side interface library connecting software and hardware layers
Write targeted and randomized tests to systematically surface corner cases and rare event sequences
Partner across hardware, architecture and software teams to translate specifications into concrete, testable properties
Requirements
Production-level Rust experience — you've shipped real systems and are comfortable with ownership, traits, generics with bounds, and enums as sum types
3+ years of engineering experience with strong fundamentals: clean, well-tested, well-documented code, and comfort with build systems, version control and CI
Genuine interest in hardware and willingness to ramp up quickly
Remote (with travel) or hybrid, based in the Bay Area
Nice to Have
Hands-on hardware experience in any form — FPGA, embedded systems, robotics, or prior hardware-adjacent roles; accelerator experience is a strong plus
Experience with foreign function interfaces (Rust FFI ideal, any cross-language experience valuable)
Exposure to formal methods, model checking or property-based testing (e.g. proptest, Kani)
Understanding of computer architecture: pipelines, caches, memory hierarchies, on-chip interconnects
Familiarity with ML systems/frameworks
What Sets This Role Apart
Tool-building culture: the design flow is built in-house, generated from a custom modelling layer rather than relying on legacy, vendor-driven methodology
AI-native flow: active work integrating AI tooling (retrieval, knowledge graphs, automated reasoning) directly into design and verification
Real ownership: your work feeds directly into production silicon at a company scaling fast
Flexible scope: significant freedom to shape what you work on, from core modelling and tooling through to deeper architecture involvement
Package & Process
Compensation is highly competitive, with strong equity and full benefits.
Show more Show less