Applied Physics Specialist

Alignerr; Glasgow; Remote; GBP 69,000 - 138,000; Part time

Job summary

Alignerr invites a PhD-level Applied Physicist to stress-test AI systems by probing physical reasoning across modules. This remote hourly contract focuses on rigorous problem design and validation in physics.

You will author gold-standard solutions, audit model outputs for consistency with first principles, and document failure modes from classical to quantum regimes. Independent, meticulous work is essential.

Qualifications

• PhD in Applied Physics or related field.
• Deep command of classical mechanics, EM, statistical and quantum mechanics.
• Excellent ability to communicate complex physics in clear English.
• Independent, self-motivated with rigorous scientific approach.
• No prior AI experience required.

Responsibilities

• Design PhD-level open-ended physics problems with multi-step reasoning.
• Write precise, step-by-step golden solutions with correct constants.
• Audit AI reasoning for physical consistency and first principles.
• Provide structured feedback to improve physics-informed model behavior.
• Document failure modes across quantum to thermodynamics.

Skills

PhD-level physics

Strong physics fundamentals

Clear scientific communication

Education

PhD in Applied Physics or related field

Tools

MATLAB

COMSOL

Python (NumPy/SciPy)

Job description

Applied Physics Specialist (AI Training)

About The Role

What if your deep expertise in physics could directly influence how AI understands the fundamental laws of the universe? We're looking for PhD-level Applied Physicists to stress-test cutting-edge Large Language Models — exposing the gaps in their physical reasoning and helping shape the next generation of scientifically grounded AI.

This is a fully remote, flexible contract role. No prior AI experience needed — just a mastery of physics and an uncompromising eye for scientific rigor.

• Organization: Alignerr
• Type: Hourly Contract
• Location: Remote
• Commitment: 10–40 hours/week

What You'll Do

• Design Advanced Problems: Craft PhD-level, open-ended physics problems requiring multi-step logical reasoning and rigorous mathematical derivation — think qualifying exam difficulty and beyond
• Author Ground-Truth Solutions: Write precise, step-by-step "golden responses" with flawless physical constants, unit conversions, and logical flow
• Audit AI Reasoning: Evaluate AI-generated simulations and proofs for physical consistency — pinpointing exactly where models hallucinate physics that violates first principles
• Refine Model Behavior: Provide structured, expert feedback to improve physics-informed reasoning across topics like boundary conditions, conservation laws, and symmetry principles
• Document Failure Modes: Build a rigorous record of how and where AI reasoning breaks down — from quantum mechanics to thermodynamics

Who You Are

• Holds a PhD (completed or in final stages) in Applied Physics, Physics, Engineering Physics, or a closely related field
• Deep command of the core pillars: Classical Mechanics, Electrodynamics, Statistical Mechanics, and Quantum Mechanics
• Exceptional ability to communicate complex physical phenomena and mathematical derivations in clear, structured English
• Uncompromising precision with units, scientific notation, and the logical integrity of a proof
• Self-motivated and reliable when working independently on technical tasks
• No prior AI or data annotation experience required

Nice to Have

• Experience with data annotation, scientific dataset evaluation, or quality assurance workflows
• Proficiency with MATLAB, COMSOL, Python (NumPy/SciPy), or similar tools
• Background in research spanning multiple subfields of physics
• Familiarity with AI tools or language model behavior as an end user

Why Join Us

• Work on high-impact AI projects in direct collaboration with leading AI research labs
• Fully remote and flexible — work on your own schedule, from anywhere
• Freelance autonomy with the structure of meaningful, technically rigorous work
• Gain firsthand exposure to how state-of-the-art LLMs are trained and evaluated
• Contribute to ensuring that AI systems genuinely respect the laws of physics
• Potential for ongoing work and contract extension as new projects launch