Remote | AWS Trainium Kernel Engineer (NKI) — $60–$80/hour

We are sharing a specialised part-time consulting opportunity for experienced kernel engineers with hands-on expertise in the Neuron Kernel Interface (NKI), AWS Trainium/Inferentia2 hardware, low-level performance optimisation, and CUDA-to-NKI migration.

This role focuses on evaluating NKI kernel-development tasks for technical correctness, hardware appropriateness, numerical fidelity, and performance quality. Selected experts will review Trainium-specific implementations, migration decisions, memory-management strategies, profiling results, and cross-platform numerical behaviour while providing clear, rubric-based technical feedback.

Key Responsibilities

NKI Kernel Development Review

• Evaluate kernels developed using the Neuron Kernel Interface (NKI)
• Assess whether implementations appropriately target AWS Trainium and Inferentia2 hardware
• Review low-level computation patterns for technical correctness
• Identify inefficient, incorrect, or hardware-inappropriate implementation choices
• Apply practical judgement grounded in hands-on NKI development experience

CUDA-to-NKI Migration

• Review migrations of existing CUDA kernels to NKI
• Assess whether computational semantics are preserved across platforms
• Identify translation errors, unsupported assumptions, or inefficient migration strategies
• Evaluate whether NKI implementations appropriately account for Trainium architecture
• Distinguish faithful migrations from implementations that merely reproduce surface-level CUDA structure

Tile-Based Computation

• Assess tile decomposition and computation strategies
• Review partitioning decisions against NKI execution constraints
• Evaluate whether kernels make effective use of available compute resources
• Identify inefficient tiling or data-movement patterns
• Assess whether implementation choices align with NKI programming requirements

Memory Hierarchy Management

• Review use of SBUF, PSUM, and HBM
• Assess data placement and movement across Trainium memory hierarchies
• Evaluate memory-bandwidth utilisation and locality
• Identify unnecessary transfers or memory bottlenecks
• Review implementation decisions affecting on-chip memory efficiency

DMA & Data Movement

• Evaluate DMA orchestration within NKI kernels
• Review sequencing of computation and data-transfer operations
• Identify stalls, inefficient transfer patterns, or synchronisation issues
• Assess whether data movement appropriately overlaps with computation
• Evaluate implementation choices affecting pipeline utilisation

Trainium Performance Optimisation

• Review Trainium-specific optimisation strategies
• Assess NeuronCore pipeline utilisation, tensor-engine throughput, and memory-bandwidth behaviour
• Identify performance bottlenecks within kernel implementations
• Evaluate whether optimisation decisions are supported by profiling evidence
• Review trade-offs affecting throughput, latency, and resource utilisation

Numerical Correctness

• Evaluate numerical consistency between GPU and Trainium implementations
• Review differences caused by accumulation order, rounding behaviour, and mixed-precision semantics
• Assess appropriate tolerances for cross-platform comparisons
• Identify numerical discrepancies that indicate implementation defects
• Distinguish expected hardware-level variation from substantive correctness problems

Precision & Data Types

• Review kernels using supported formats such as FP32, BF16, FP8, and INT8
• Assess precision choices against computational requirements
• Evaluate mixed-precision behaviour and numerical stability
• Identify inappropriate casting or accumulation strategies
• Review whether performance gains are achieved without compromising required correctness

AWS Neuron Ecosystem

• Evaluate implementations using the AWS Neuron SDK
• Review interactions between kernel code, compilation, and Trainium execution
• Assess compiler-related behaviours where relevant
• Apply familiarity with NKI kernel libraries and Neuron tooling
• Identify implementation issues arising from platform-specific constraints

Benchmarking & Validation

• Review benchmark results for Trainium workloads
• Assess performance comparisons and experimental methodology
• Evaluate workloads running on Trn1 or Trn2 instances where applicable
• Determine whether claimed performance improvements are supported by evidence
• Identify benchmarking methodologies that could produce misleading conclusions

Rubric-Based Technical Evaluation

• Assess assigned kernel-development tasks against structured technical criteria
• Provide clear written explanations supporting evaluation decisions
• Reference specific implementation, profiling, or numerical evidence
• Apply evaluation standards consistently across assignments
• Distinguish valid optimisation alternatives from technically flawed approaches

Ideal Profile

• 2+ years of hands-on experience developing or optimising kernels using the Neuron Kernel Interface (NKI)
• Professional experience targeting AWS Trainium or Inferentia2 hardware
• Strong understanding of tile-based computation
• Deep familiarity with SBUF, PSUM, and HBM memory management
• Strong knowledge of partition-dimension constraints and DMA orchestration
• Demonstrated experience evaluating or performing CUDA-to-NKI migrations
• Familiarity with Trainium-specific performance profiling
• Experience assessing NeuronCore pipeline utilisation, tensor-engine throughput, and memory-bandwidth bottlenecks
• Strong understanding of cross-platform numerical correctness and mixed-precision behaviour
• Direct experience with the AWS Neuron SDK, Neuron Compiler internals, or NKI kernel libraries is preferred
• Prior CUDA or Triton kernel development experience is advantageous
• Familiarity with NeuronCore-v2 architecture and supported numerical formats is preferred
• Experience benchmarking ML workloads on Trn1 or Trn2 instances is advantageous
• Strong written communication and ability to provide precise technical feedback

Engagement Details

• Part-time independent contractor engagement
• Fully remote within the United States
• Flexible scheduling based on project requirements
• Compensation: $60–$80/hour
• Work focuses on NKI kernel development, Trainium performance optimisation, CUDA migration, numerical correctness, and technical quality evaluation
• Projects may be extended, shortened, or concluded based on project needs and performance
• Work must be completed without using confidential or proprietary information belonging to any employer, client, institution, or other third party
• H1-B and STEM OPT support is unavailable for this engagement

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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