Applied Scientist (Remote)

Joblet-AI; India; A distancia; INR 7.776.000 - 15.551.000; A tiempo parcial

Descripción de la vacante

Joblet-AI is seeking highly skilled Machine Learning experts for an AI training project. The contractor will develop models, training pipelines, and inference systems, with a focus on Python-based workflows and reproducible processes.

The role involves coding-heavy tasks including implementing components, data pipelines, and numerical methods. Candidates should have a MSc/PhD in a quantitative field and substantial ML experience, plus proficiency with Python and coding agents.

Formación

• Master's degree or PhD in Computer Science, Machine Learning, AI, Applied Mathematics, Statistics, Engineering, or related quantitative field.
• Strong professional or research experience in machine learning.
• Proficient in Python and coding agents.
• Equivalent tools may be considered when demonstrating depth.

Responsabilidades

• 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.

Conocimientos

Python programming

Machine learning

Research experience

Experimentation/debugging

Educación

Master's or PhD in CS/ML/AI/Applied Math

Herramientas

Python tooling

Open-source tools

Descripción del empleo

• Role Title: Applied Scientists — Machine Learning (MSc. or PhD)
• Role Type: Contractor (~15 hours per week)
• Location: Global, fully remote
• Applied Scientists — Machine Learning (MSc. or PhD)
• Pay: $60–$120/hour
• 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 is a very coding heavy role. Candidates but have experience using coding agents with python in their workflow.

What You’ll Work On

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

Required Qualifications

• 1. A master’s degree or PhD in Computer Science, Machine Learning, Artificial Intelligence, Applied Mathematics, Statistics, Engineering, or a closely related quantitative discipline.
• 2. Strong professional or research experience in machine learning.
• 3. Practical proficiency with Python and coding agents.
• 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.

Compensation Structure

Compensation 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

1. 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.

What You’ll Do

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