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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