Remote Physics AI Trainer (PhD) - 34878

Turing

Boston (MA)

Remote

USD 71,300 - 107,000

Part time

14 days+

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Job summary

A leading AI company in Boston is seeking researchers with a strong Physics background to evaluate large language models. The ideal candidate, with a PhD or postdoc in Physics, will work on complex tasks impacting AI performance in STEM. This role requires strong analytical skills, effective communication, and the ability to work remotely.

Qualifications

Enrolled in or completed a PhD/Postdoc in Physics, Applied Physics, or a related field.
Ability to work independently in a remote environment.
Access to a reliable computer and internet.

Responsibilities

Evaluate large language models in solving complex physics problems.
Complete specific research tasks to find model weaknesses.
Participate actively in team discussions.

Skills

Analytical skills

Communication skills

Research independence

Education

PhD/Postdoc in Physics or related field

Job description

1 day ago Be among the first 25 applicants

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:

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.
For a 1D Brownian motion starting at the origin, define the last return time to zero and the time spent on the positive axis as fractions of total time. Find the exact joint probability distribution of these scaled times, expressed using the Heaviside function to specify its support.

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.

Seniority level