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ML Engineer

micro1Remote🌍 Remote
ContractMid Level
$0k - $0k
per year
👁️ 0 views📝 0 applicationsPosted 9/12/2026Expires 11/11/2026
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Job Description

Pay: $100–$150/hour Location: Global, fully remote Job Type: Contractor (~15 hours per week) Schedule: Flexible—you choose the hours and days you work, including weekends if desired We are looking for highly skilled Machine Learning Experts to contribute to an AI training project involving model development, training and inference systems, numerical computing, performance optimization, and Python.

The work involves creating, solving, reviewing, and validating challenging machine-learning engineering tasks.

A representative task may require implementing or modifying a model, constructing a reproducible training or inference workflow, optimizing memory or throughput, debugging numerical or system-level failures, and verifying that the resulting implementation satisfies objective correctness and performance requirements.

This role is designed for experienced ML engineers and researchers who understand the systems beneath high-level APIs. Candidates should have meaningful practical experience with multiple tools from the modern ML stack and be able to explain what they personally built, optimized, or operated.

What You’ll Work On Develop and validate machine-learning models, training pipelines, inference systems, and supporting infrastructure. Implement model components, data pipelines, evaluation systems, and numerical methods. Build reproducible programmatic workflows using Python and command-line tools.

Work with tensor operations, automatic differentiation, model architectures, tokenization, batching, and generation. Optimize training or inference for latency, throughput, memory usage, and hardware utilization.

Diagnose numerical instability, incorrect tensor behavior, memory bottlenecks, distributed-system failures, and performance regressions. Compare model implementations and determine whether results are correct and reproducible.

Review AI-generated code and technical solutions for correctness, efficiency, and engineering quality. Design objectiv

Required Skills

PythonMachine LearningModel DevelopmentTraining PipelinesInference SystemsNumerical ComputingPerformance OptimizationTensor OperationsAutomatic DifferentiationModel ArchitecturesTokenizationBatchingGenerationCommand-Line ToolsData PipelinesEvaluation SystemsNumerical MethodsDistributed SystemsHardware UtilizationDebugging

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