Remote | Systems Performance Engineer — $65–$105/hour

We are sharing a specialised full-time consulting opportunity for US-based performance engineers with strong experience in systems programming, low-level optimisation, runtime performance, and production development using C++, Python, or Rust.

This role supports a high-impact generative AI initiative focused on developing and evaluating advanced performance-engineering tasks for frontier model training and inference systems. Selected engineers will design technically challenging problems, produce rigorous solutions, assess model-generated outputs, and establish evaluation standards across systems optimisation, compiler engineering, runtime performance, latency, throughput, and memory efficiency.

Key Responsibilities

Systems Performance Optimisation

• Analyse performance across production systems, AI workloads, runtime environments, and supporting infrastructure
• Identify bottlenecks affecting latency, throughput, memory consumption, and computational efficiency
• Evaluate systems-level optimisation strategies across C++, Python, and Rust applications
• Guide research and engineering teams on runtime behaviour, resource utilisation, and performance trade-offs

Technical Task & Solution Development

• Design challenging performance-engineering tasks grounded in realistic systems and infrastructure scenarios
• Write accurate, technically rigorous, and well-structured solutions
• Develop problems involving profiling, benchmarking, concurrency, memory management, runtime efficiency, and systems architecture
• Ensure tasks reflect practical performance challenges found in production AI and software environments

Code & Architecture Evaluation

• Review technical solutions written in C++, Python, Rust, or related systems languages
• Assess implementation correctness, computational complexity, memory behaviour, and execution efficiency
• Evaluate concurrency models, data structures, compiler behaviour, and runtime design decisions
• Identify optimisation opportunities while considering maintainability, reliability, and system-level trade-offs

Evaluation Frameworks & Technical Feedback

• Compare alternative technical solutions and determine which approach is more accurate and effective
• Provide clear written feedback on performance, correctness, systems design, and optimisation quality
• Develop detailed rubrics for evaluating performance-engineering tasks across AI workloads
• Collaborate with other technical specialists to maintain consistency and accuracy across training data

Ideal Profile

Strong candidates may have:

• At least 2 years of dedicated professional experience in performance engineering, systems programming, or low-level optimisation
• Deep hands-on expertise in C++, Python, or Rust
• Working familiarity with the other listed languages is highly valuable
• A measurable record of improving production-system latency, throughput, scalability, or memory efficiency
• Strong knowledge of profiling, benchmarking, concurrency, memory management, and runtime behaviour
• Demonstrable professional growth and increasing technical responsibility
• Strong written communication and the ability to explain complex technical decisions clearly
• Reliable availability for a full-time, 40-hour weekday schedule

Educational Background

• A degree in computer science, software engineering, computer engineering, applied mathematics, or a related technical field is highly relevant
• Graduate-level education in systems engineering, compilers, distributed computing, or high-performance computing may be helpful
• Equivalent professional experience in production systems or performance optimisation may also be considered
• Advanced work involving operating systems, runtime development, compiler technology, or large-scale infrastructure is especially valuable

Nice to Have

• Experience optimising AI training, inference, or high-performance computing workloads
• Familiarity with compiler internals, intermediate representations, code generation, or runtime systems
• Knowledge of CPU and GPU architecture, cache behaviour, vectorisation, and parallel execution
• Experience using profilers, tracing systems, benchmarking frameworks, and performance-analysis tools
• Familiarity with distributed systems, multithreading, asynchronous execution, or memory allocators
• Previous involvement in technical review, engineering mentorship, or rubric development
• Experience collaborating with research scientists, infrastructure teams, or compiler engineers

Why This Opportunity

• Contribute to advanced generative AI training and inference initiatives
• Apply deep expertise in systems programming and production performance optimisation
• Work on challenging problems spanning runtime behaviour, compilers, memory, and computational efficiency
• Influence the quality of technical training data used in frontier AI development
• Join a full-time remote engagement with competitive hourly compensation

Contract Details

• Full-time W-2 contingent employment arrangement
• Fully remote role available to candidates based in the United States
• Expected commitment of 40 hours per week during weekdays
• This engagement requires full professional availability without conflicting employment or external commitments
• Competitive rates between $65–$105 per hour depending on expertise and project scope
• Immediate availability is preferred
• Work may include onboarding, technical calibration, and ongoing quality-review activities
• Project scope and duration may be adjusted according to programme requirements and performance

About the Platform

This opportunity is available through 24-MAG LLC. We connect experienced professionals with remote consulting opportunities across technical, evaluation, and project-based workstreams.

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