ML Researcher

Flashmind Labs; Dubai; On-site; AED 420,000 - 660,000; Full time

Job summary

Flashmind Labs in Dubai is seeking researchers to push the boundaries of foundation models optimized for truth. You will contribute to novel training objectives, uncertainty-aware architectures, and evaluation methods, collaborating with scientists and engineers.

The role focuses on empirical outcomes, calibration, and robustness across distribution shifts, requiring strong independent work and a track record in advanced research.

Qualifications

• PhD or equivalent in ML/CS or related field.
• Proficiency in Python and PyTorch.
• Ability to design, run, and analyze experiments independently.
• Understanding of large-scale training and accelerator-based compute environments.

Responsibilities

• Develop novel training objectives for factual accuracy and calibration.
• Design architectures that handle uncertainty and distributional information.
• Build training pipelines grounded in empirical outcomes.
• Develop evaluation frameworks for calibration, tail event accuracy, and robustness under distribution shift.

Education

PhD or equivalent in ML/CS or related field

Tools

Python

PyTorch

Job description

About Flashmind Labs

We are building foundation models that engage with reality directly and evolve beyond their training data. Models that are calibrated, unconstrained in their pursuit of truth, and built for decisions with real consequences.

We are a focused team of researchers and engineers in Dubai. Come build the future of learning systems with us.

About This Role

We're looking for researchers to push the boundaries of what foundation models can do when optimized for truth. You will work on novel training objectives, architectures designed to preserve calibration and handle uncertainty natively, and evaluation frameworks grounded in empirical outcomes.

You will work with a team of scientists and engineers on core research efforts, including:

• Novel training objectives for factual accuracy and calibration
• Architectures that handle uncertainty and distributional information natively
• Training pipelines grounded in empirical outcomes
• Evaluation frameworks for measuring calibration, tail event accuracy, and robustness under distribution shift

Minimum Qualifications

• PhD or equivalent experience in ML, CS, or a related field
• Proficiency in Python and PyTorch
• Ability to design, run, and analyze experiments independently
• Understanding of large-scale training and accelerator-based compute environments

Preferred Qualifications

• Deep expertise in at least one of: alignment methods (RLHF, DPO), calibration, loss function design, or model architectures
• Demonstrated record of contributing to advanced research via publications and/or major model releases
• Experience with distributed training at scale
• Background in epistemology, calibration, or decision theory