Rust Developer

Job Summary

The Remote Rust Developer will design, build, and secure low-latency backend architectures while executing rigorous, multi-day testing cycles to analyze LLM code-generation models. This role focuses on optimizing endpoints, planning schema migrations, and delivering highly structured engineering feedback directly to AI research groups to improve the performance of code automation tools.

Qualification

• Core Language Dominance: Expert-level mastery of the Rust language, including deep familiarity with memory safety mechanics, concurrency patterns, and the broader Rust ecosystem.
• Modern DevTools Fluency: Advanced, day-to-day reliance on AI-augmented IDEs and development environments, with a strong preference for Cursor.
• Linguistic & Communication Mechanics: Flawless written and verbal English skills, with an exceptional knack for authoring granular technical incident records, stack traces, and bug logs.
• Environmental Agility: Proven capacity to work autonomously under tight schedules within highly confidential, rapid-iteration remote research labs.

Experience

Production-Grade Backend Systems Engineering

• Field Tenure Alignment: A minimum of five (5) years of active, professional experience operating as a core backend developer on live, scaled software systems.
• API Architecture Mastery: Hands-on history building, securing, and maintaining scalable RESTful and GraphQL API endpoints.
• Data Hygiene & Infrastructure Operations: Comprehensive understanding of backend data validation frameworks, complex error handling strategies, schema designs, performance tuning, and secure database migrations.

Tooling Evaluation & Open Source Footprint (Preferred)

• Open Source Contributions: Verifiable, visible contributions to notable open-source repositories (e.g., active GitHub profile, accepted PRs, or popular ecosystem crates).
• Experimental Tooling Evaluation: Past experience designing, benchmarking, or auditing experimental development workflows, compilers, or testing automation pipelines.
• AI Advancements Passion: A clear, active enthusiasm for machine learning innovations, prompt engineering, and the evolution of AI within the software development life cycle.

Key Responsibilities

API Engineering & Database Optimization (40%)

• Develop High-Performance APIs: Design, implement, and optimize highly secure, scalable REST and GraphQL endpoints inside backend microservices.
• Govern Backend Security: Write robust validation libraries, bulletproof error-handling layers, and advanced encryption/authorization practices to protect system components.
• Manage Database Lifecycles: Plan and execute complex database migrations, database indexing strategies, and structural schema modifications without causing downtime.

Model Evaluation & Intensive Testing Bursts (40%)

• Execute Workflow Testing: Participate in targeted, 4-day intensive testing blocks, pushing new AI models to their breaking points within daily real-world software workflows.
• Audit Code Generation: Review machine-generated Rust snippets, evaluating how effectively models manage borrow-checker constraints, life times, and architectural design patterns.
• Document Technical Anomalies: Author highly accurate post-burst surveys, logging precise incident details, execution traces, system bugs, and screenshots to isolate model regression.

Cross-Functional Research Collaboration (20%)

• Interface with Research Scientists: Engage directly with frontier AI research teams within dedicated channels to unpack empirical findings, critique code logic, and propose model optimization strategies.
• Advance Developer Workflows: Ideate and share actionable feedback on IDE integration, context retrieval mechanisms, and user experience patterns inside Cursor.

Expected Outputs & Deliverables

• Clean, well-tested, and secure Rust source code for production backend APIs.
• Scalable GraphQL and REST schema designs alongside fully documented data migration files.
• Highly detailed technical incident reports, bug traces, and failure-mode analyses following each 4-day testing burst.
• Deep-dive post-evaluation surveys providing structural recommendations for LLM model improvement.

Skills Required:

• Computer / Software / It / Data