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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