ML Engineer

Remotebridge; United States; À distance; USD 110 000 - 207 000; Temps partiel

Résumé du poste

Frontier AI Lab is hiring a part-time, contractor ML Engineer for a fully remote role. You will develop and optimize machine-learning models, training pipelines, and inference systems, leveraging Python and modern ML frameworks. The position is global and hourly-based, with flexible hours.

The role emphasizes reproducible workflows, numerical computing, and performance tuning, requiring strong ML background and hands-on experience with PyTorch, JAX, NumPy/SciPy, and open-source tools.

Qualifications

• Master’s degree or PhD in CS/ML/AI/Applied Math or related field
• Strong ML experience in industrial or research setting
• Proficiency with Python and coding agents

Responsabilités

• Develop and validate machine-learning models, training pipelines, and inference systems
• Implement model components, data pipelines, evaluation systems, and numerical methods
• Build reproducible workflows using Python and command-line tools

Connaissances

Python programming

Machine learning

Numerical computing

Formation

Master's degree or PhD

Outils

PyTorch

JAX

NumPy

SciPy

SGLang

vLLM

llama.cpp

Transformers

Tokenizers

Description du poste

# ML EngineerFrontier AI Lab · Remote · Full-time## Ready to see if you fit?Upload a resume once, then run an honest fit check on this role in seconds. Access is free and always will be.Sign up to check your fit## About the roleML EngineerPay: $80–$150/hourLocation: Global, fully remoteJob Type: Contractor (~15 hours per week)Schedule: Flexible—you choose the hours and days you work, including weekends if desiredWe 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 is a very coding heavy role. Candidates but have experience using coding agents with python in their workflow.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.Required Qualifications• A 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 and coding agents.Relevant tools may include:• PyTorch• JAX• NumPy and SciPy• SGLang• vLLM• llama.cpp• Hugging Face Transformers• Hugging Face Tokenizers Equivalent tools may also be considered when the candidate demonstrates directly relevant depth.Experience at a well-established technology company, AI laboratory, research organization, or other recognized engineering environment is strongly preferred. Exceptional open-source or academic experience may also qualify.Process• Apply to the role and complete the screening questions.• Complete an AI interview of approximately 30 minutes.• Complete the hiring manager review. Compensation StructureCompensation is output-based. Experts are paid per task that meets the project specifications. The time required to complete each task may vary depending on the expert’s experience and workflow.Minimum submission requirements apply.Start Timeline & Availability• We typically fill roles within 48 hours and are looking for experts who are ready to begin immediately. If selected, you will be expected to start your first task within 24–48 hours of completing onboarding.