Remote Physics AI Trainer (PhD) - 34440

Turing; Hyderabad; Presencial; INR 223.200 - 446.400; A tiempo parcial

Ventajas ofrecidas por este puesto de trabajo

Collaboration with global experts

Exposure to cutting-edge research

Flexible work hours

Descripción de la vacante

A leading AI company in Hyderabad seeks researchers with strong Physics backgrounds to evaluate large language models (LLMs). Candidates must have or be pursuing a PhD and possess strong analytical skills. The role involves solving complex physics problems and participating in a remote research environment with flexible time commitments.

Formación

• Must have completed or be currently pursuing a PhD.
• Strong ability to solve advanced physics problems.
• Clarity in explaining complex topics.

Responsabilidades

• Help fine-tune and evaluate large language models.
• Conduct research on complex physics problems.
• Participate in remote research environment.

Conocimientos

Strong analytical skills

Clear communication

Research independence

Educación

PhD in Physics or related field

Descripción del empleo

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We are looking for researchers with strong Physics and analytical backgrounds to help fine-tune and evaluate large language models (LLMs). Your work will help LLMs get better at understanding and solving complex physics problems, such as multi-step conceptual or quantitative questions in areas like mechanics, electromagnetism, thermodynamics, and quantum theory.

Research Objectives

As a contributor to this research-focused initiative, you may be asked to complete the following or similar tasks:

• An electron is emitted radially from the surface of a long, current-carrying cylindrical conductor. Due to the magnetic field generated by the current, determine the maximum radial distance the electron reaches from the conductor's axis.
• A high-energy pion travels through a material, losing energy at a constant rate. Given its rest mass and proper lifetime, calculate the probability that it decays within a certain distance from its origin.
• These tasks are designed to find model weaknesses and improve training and evaluation workflows.

Candidate Profile

• Academic Background: Enrolled in or completed a PhD/Postdoc in Physics, Applied Physics, or a related field.
• Analytical Skills: Strong ability to solve advanced physics problems with clear and structured reasoning.
• Communication Skills: Clarity in explaining complex topics and providing feedback with precision.
• Research Independence: Ability to work independently in a remote research environment and participate actively in team discussions.
• Technical Setup: Access to a reliable computer and internet setup to participate in remote assignments.

Note: This position is open only to candidates who have completed or are currently pursuing a Ph.D.; applications that do not meet this criterion will not be considered.

Time Commitment & Contract Details

• Commitments Required: at least 4 hours per day and minimum 20 hours per week with 4 hours of overlap with PST. (We have 3 options of time commitment: 20 hrs/week, 30 hrs/week, or 40 hrs/week)
• Engagement type: Contractor assignment/freelancer (no medical/paid leave)
• Duration of contract: 1 month; [expected start date is next week]
• Selection Process: Task-based assessment for shortlisted candidates, followed by a contract offer with defined timelines and workload.

About Turing

Turing is one of the world’s fastest-growing AI companies, accelerating the advancement and deployment of powerful AI systems.

Turing helps customers in two ways: Working with the world’s leading AI labs to advance frontier model capabilities in thinking, reasoning, coding, agentic behavior, multimodality, multilinguality, STEM, and frontier knowledge; and leveraging that work to build real-world AI systems that solve mission-critical priorities for companies.

Why work with Turing?

• Work on research that directly impacts the future of language model performance in mathematics and STEM.
• Collaborate with a global network of experts shaping next-generation AI benchmarks.
• Gain exposure to cutting-edge model evaluation strategies in an academic–industry hybrid setting.