ML Research Engineer, Inference and GPU Kernels

OpenTrain AI, Inc.; United States; Remoto; USD 138.000 - 234.000; Part-time

Vantaggi offerti da questo lavoro

Remote work

Descrizione del lavoro

OpenTrain AI, Inc. is seeking a remote, part-time contractor to create technical problems and grading guides and to assess AI answers on machine learning inference, GPU kernels, inference serving, and large-model deployment.

The work focuses on judging model answers and explaining what strong technical reasoning looks like, rather than building production systems. You will design realistic scenarios, write reference solutions and grading rubrics, review AI-generated responses for accuracy and

Competenze

• Pay: $100 to $170 per hour.
• Schedule: 20+ hours per week, flexible asynchronous work.
• Location: Fully remote; eligible countries.
• Language: Strong written English for technical explanations, reference answers, grading rubrics, and feedback.

Mansioni

• Design realistic technical scenarios and problem sets.
• Write reference solutions and grading rubrics that define correctness, depth, and technical judgment.
• Review AI-generated answers for accuracy, reasoning quality, and understanding of the subject.
• Provide structured feedback and write clear technical explanations of your decisions.

Conoscenze

Strong written English

Technical judgment

Feedback writing

Formazione

PhD or doctorate

Master's degree with strong research record

Strumenti

SGLang

vLLM

Mamba/Mamba2

TensorRT-LLM

GPU kernel development

Descrizione del lavoro

Create technical problems and grading guides, then assess AI answers on machine learning inference and GPU kernels. Remote, flexible contractor work pays $100 to $170 per hour and requires 20+ hours per week.

Remote Hourly · $100–$170/hr

$100–$170/hr

Compensation

Contract, part-time

Engagement

Remote

Location

Posted

Open in 95 countries

Portugal + more

• Albania
• Algeria
• Antigua and Barbuda
• Armenia
• Australia
• Austria
• Bahamas
• Bahrain
• Belgium
• Benin
• Bhutan
• Bosnia and Herzegovina
• Botswana
• Brunei
• Bulgaria
• Canada
• Costa Rica
• Côte d’Ivoire
• Croatia
• Cyprus
• Czechia
• Denmark
• Ecuador
• El Salvador
• Estonia
• Ethiopia
• Finland
• France
• Gambia
• Germany
• Greece
• Guatemala
• Guyana
• Hong Kong
• Hungary
• Iceland
• India
• Indonesia
• Ireland
• Israel
• Italy
• Jamaica
• Jordan
• Kenya
• Kuwait
• Latvia
• Liechtenstein
• Lithuania
• Luxembourg
• Madagascar
• Malaysia
• Malta
• Mauritius
• Mexico
• Moldova
• Mongolia
• Morocco
• Mozambique
• Namibia
• Netherlands
• New Zealand
• North Macedonia
• Norway
• Oman
• Pakistan
• Panama
• Peru
• Philippines
• Poland
• Portugal
• Qatar
• Romania
• Rwanda
• Saint Lucia
• Senegal
• Serbia
• Singapore
• Slovakia
• Slovenia
• South Africa
• Spain
• Sri Lanka
• Sweden
• Switzerland
• Taiwan
• Tanzania
• Thailand
• Trinidad and Tobago
• Tunisia
• Türkiye
• United Arab Emirates
• United Kingdom
• United States
• Uzbekistan
• Vietnam

The work

You will help evaluate AI models on difficult technical problems in machine learning inference, GPU kernels, inference serving, and large-model deployment. The work focuses on judging model answers and explaining what strong technical reasoning looks like, rather than building production systems.

• Design realistic technical scenarios and problem sets.
• Write reference solutions and grading rubrics that define correctness, depth, and technical judgment.
• Review AI-generated answers for accuracy, reasoning quality, and understanding of the subject.
• Provide structured feedback and write clear technical explanations of your decisions.

What it pays and takes

• Pay: $100 to $170 per hour.
• Schedule: 20+ hours per week, with flexible, asynchronous work.
• Location: Fully remote, with availability limited to eligible countries.
• Work type: Part-time contractor.
• Language: Strong written English for technical explanations, reference answers, grading rubrics, and feedback.
• Education: A PhD or doctorate, or a master's degree supported by a strong research record.
• Helpful background: Current or recent hands-on work in machine learning inference, GPU kernels, inference serving, kernel optimization, or large-model deployment. Research publications, open-source contributions, recognized work, or experience with code review, peer review, grading, or editing can help.
• Useful tools and systems include SGLang, vLLM, Mamba or Mamba2, TensorRT-LLM, and GPU kernel development.

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