We are sharing a specialised consulting opportunity for experienced machine learning researchers with hands-on expertise training and improving deep learning models end-to-end across computer vision and language.
This role supports advanced empirical machine learning research across model training, efficiency, robustness, multimodal systems, and post-training. Selected researchers will work on well-scoped but open-ended technical problems involving image models, language models, adversarial robustness, model compression, multilingual learning, and efficient training under constrained data and compute budgets.
Key Responsibilities
Model Training & Research
Train image classifiers and generative image models from scratch
Fine-tune and post-train open-weight language models
Design and execute empirical machine learning experiments
Diagnose optimisation, convergence, data-quality, and training-stability issues
Develop approaches that maximise performance under limited data, compute, or model-size budgets
Computer Vision & Generative Modelling
Train image classifiers for challenging recognition tasks
Develop models for fine-grained recognition with limited examples
Train diffusion models, GANs, VAEs, flow-based models, or comparable generative architectures
Evaluate generative models using metrics such as FID
Improve sample quality while controlling training cost and parameter count
Robustness & Model Efficiency
Develop models that remain reliable under adversarial inputs
Apply adversarial training approaches such as PGD-based training or TRADES
Evaluate robust accuracy under established threat models
Investigate robustness–accuracy trade-offs and robust overfitting
Apply quantisation, pruning, knowledge distillation, and related model-compression techniques
Optimise models for strict memory, size, or latency constraints
LLM Post-Training & Behaviour
Conduct supervised fine-tuning and preference optimisation of open-weight language models
Work with methods such as DPO, RLHF, or RLAIF where relevant
Develop training datasets using synthetic generation, weak supervision, noisy supervision, or rejection sampling
Improve multi-turn conversational behaviour including resistance to persuasion and sycophancy
Develop approaches for calibrated confidence and appropriate response to corrections
Modify targeted behaviours while preserving broader model capabilities
Multilingual & Low-Resource Modelling
Train multilingual or low-resource language models
Develop tokenisation strategies across diverse scripts and language families
Address highly imbalanced multilingual training datasets
Explore sampling strategies and cross-lingual transfer
Improve model performance in data-constrained language settings
Ideal Profile
Strong candidates may have:
At least 3 years of machine learning research experience, including qualifying PhD research
Hands-on experience training deep learning models end-to-end
Strong proficiency with PyTorch, JAX, TensorFlow, or comparable machine learning frameworks
Deep expertise in at least one relevant research area such as adversarial robustness, computer vision, generative modelling, LLM post-training, or multilingual pre-training
Experience designing and running rigorous empirical experiments
Strong understanding of optimisation, model evaluation, and experimental methodology
Ability to diagnose complex model-training and performance issues
Strong technical writing and research communication skills
Educational Background
A degree in computer science, machine learning, artificial intelligence, mathematics, statistics, engineering, or a related technical field is highly relevant
PhD research in machine learning or a closely related area may count toward the professional experience requirement
Candidates may also demonstrate equivalent research strength through significant industry work, publications, or impactful open-source contributions
A strong academic, industry, or independent research track record is particularly valuable
Nice to Have
Experience with scaling laws or training-efficiency research
Background in curriculum learning or data ordering
Experience building machine learning benchmarks
Knowledge of benchmark contamination detection and prevention
Familiarity with statistically rigorous model comparison
Experience with uncertainty estimation or model calibration
Expertise in synthetic data or data augmentation
Publications in recognised machine learning or AI venues
Experience at a major AI, technology, or research organisation
Significant open-source machine learning contributions
Why This Opportunity
Work on cutting-edge machine learning research across vision and language
Explore open-ended empirical problems with meaningful technical depth
Conduct research spanning robustness, efficiency, generative modelling, and post-training
Collaborate with experienced AI researchers on challenging technical projects
Apply advanced ML expertise to models operating under realistic data, compute, and deployment constraints
Participate in flexible project-based work with competitive hourly compensation
Contract Details
Independent contractor role
Fully remote with flexible scheduling
Competitive rates between $95–$115 per hour depending on expertise and project scope
Work may include model training, experimentation, robustness research, model compression, post-training, multilingual modelling, and evaluation
Weekly payments via Stripe or Wise
Projects may be extended, shortened, or adjusted depending on scope and performance
Work will not involve access to confidential or proprietary information from any employer, client, or institution
About the Platform
This opportunity is available through 24-MAG LLC. We connect experienced professionals with remote consulting opportunities across technical, evaluation, and project-based workstreams.
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