CONTRACTORRemoteActive
ML Engineer
micro1 is engaging experienced Machine Learning Engineers and researchers for an AI training project involving model development, training and inference systems, numerical computing, performance optimization, and Python. Contributors will create, solve, review, and validate challenging machine-learning engineering tasks, including implementing or modifying models, building reproducible training and inference workflows, optimizing latency, throughput and memory usage, debugging numerical and system-level failures, and verifying objective correctness and performance requirements. The engagement is approximately 15 hours per week with a flexible schedule and output-based compensation.
Salary
USD100 - USD150 / hour
Department
Machine Learning & AI Engineering
Experience
Strong professional or research experience in machine learning, with practical experience using multiple tools from the modern ML stack and the ability to build, optimize, debug, and evaluate ML systems beyond high-level API usage
Curated by Farooq77 Jobs
Role fit snapshot
- Engagement
- Remote contractor | micro1 application platform
- Experience
- Recorded experience: Strong professional or research experience in machine learning, with practical experience using multiple tools from the modern ML stack and the ability to build, optimize, debug, and evaluate ML systems beyond high-level API usage
- Core expertise
- Machine Learning & AI Engineering | Machine Learning | PyTorch | JAX
- Named tools
- Python
- Geographic eligibility
- Not specified beyond Remote
- Listing dates
- Posted 2026-09-12 | Valid through 2026-10-12
- Compensation
- Recorded compensation: $100-$150/hourOutput-based compensation is also recorded in the listing.
Responsibilities
- 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 model training and 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 their results are correct and reproducible.
- Review AI-generated code and technical solutions for correctness, efficiency, and engineering quality.
- Design objective tests, benchmarks, and verification criteria for machine-learning engineering tasks.
- Clearly document technical decisions, implementation trade-offs, limitations, and failure modes.
Skills
Machine LearningPythonPyTorchJAXNumPySciPySGLangvLLMllama.cppHugging Face TransformersHugging Face TokenizersModel TrainingModel InferenceTraining PipelinesInference SystemsNumerical ComputingTensor OperationsAutomatic DifferentiationModel ArchitecturesTokenizationBatchingText GenerationPerformance OptimizationLatency OptimizationThroughput OptimizationMemory OptimizationHardware UtilizationDistributed SystemsML DebuggingModel EvaluationBenchmarkingAI Code EvaluationCommand-Line ToolsReproducible ML Workflows
Requirements
- Master's degree or PhD in Computer Science, Machine Learning, Artificial Intelligence, Applied Mathematics, Statistics, Engineering, or a closely related quantitative discipline.
- Strong professional or research experience in machine learning.
- Practical proficiency with Python.
- Meaningful experience with at least two relevant machine-learning frameworks, libraries, or inference tools.
- Strong understanding of model training, evaluation, numerical computation, or inference.
- Ability to debug machine-learning systems beyond surface-level API usage.
- Ability to explain implementation decisions, performance trade-offs, and failure modes clearly.
- Experience building reproducible technical workflows.
- Meaningful practical experience with tools such as PyTorch, JAX, NumPy, SciPy, SGLang, vLLM, llama.cpp, Hugging Face Transformers, Hugging Face Tokenizers, or equivalent technologies.
- Experience at a well-established technology company, AI laboratory, research organization, or recognized engineering environment is strongly preferred.
- Exceptional open-source or academic machine-learning experience may also qualify.
- Ability to commit approximately 15 hours per week and begin the first task within 24–48 hours of onboarding if selected.
Benefits
- Fully remote contractor opportunity open worldwide.
- Listed compensation of $100–$150 per hour.
- Output-based compensation for tasks that meet project specifications.
- Flexible schedule with approximately 15 hours of work per week.
- Freedom to choose working hours and days, including weekends if desired.
- Opportunity to work on advanced model development, training, inference, numerical computing, and performance optimization challenges.
- Direct involvement in training and evaluating advanced AI systems.
- Opportunity to work across modern machine-learning frameworks, inference engines, and numerical computing tools.
- Fast hiring process with roles typically filled within 48 hours.
Before you apply
- Confirm that your location is eligible for the role.
- Confirm the employment or contract type.
- Verify the current compensation at the official source.
- Review the required skills and experience.
- Check that the listing is still open before applying.
- Never pay a fee to submit a job application.