Manager Remote Machine Learning Jobs in New York

About the job Remote | Machine Learning Research Scientist - $95-$115/hour
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

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