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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in training and optimizing machine learning models, particularly in image classification and generative modeling. Proficient in adversarial training, model compression techniques, and deploying models under size and latency constraints.
Highest-signal resume keywords
Machine Learning Research ExperienceProficiency in PyTorch, JAX, TensorFlowAdversarial Training of Image ClassifiersModel Compression TechniquesTraining Generative Models from Scratch
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Image ClassificationGenerative ModelingAdversarial TrainingModel CompressionFine-Tuning Language ModelsTraining Diffusion ModelsQuantizationPruningKnowledge DistillationDataset Building
Soft Skills
CollaborationProblem-Solving
Industry Keywords
Machine LearningAI SystemsRobustness-Accuracy Trade-OffsTraining Efficiency TechniquesMultilingual ModelsTokenizationSynthetic Data Generation
Tech Stack
Tools & technologiesPyTorchTensorflow
About the role
Key responsibilities & impact- Train image classifiers and generative image models from scratch
- Fine-tune open-weight language models
- Conduct empirical, open-ended machine learning research
- Optimize models under limited data, compute, and model-size budgets
- Improve robustness against adversarial inputs and adversarial conversations
- Compress models to meet size and latency constraints without sacrificing accuracy
- Diagnose and resolve training issues
- Collaborate with leading AI researchers on challenging, high-impact projects
- Work on projects focused on training and enhancing AI systems
Requirements
What you’ll need- 3+ years of machine learning research experience; PhD research counts toward this requirement
- Strong experience with PyTorch, JAX, TensorFlow, or similar ML frameworks
- Degree from a top-100 university, experience at a FAANG or comparable AI company, or an equivalent research track record through publications or impactful open-source contributions
- Experience with adversarial training of image classifiers, robust accuracy evaluation, and robustness–accuracy trade-offs
- Experience with model compression, including quantization, pruning, and knowledge distillation
- Experience deploying models under hard size or latency budgets
- Experience training diffusion models, GANs, VAEs, or flow-based generative models from scratch
- Experience with FID and training-efficiency techniques for generative models
- Experience with supervised fine-tuning and preference optimisation (DPO, RLHF, RLAIF) of open-weight language models
- Experience building datasets via synthetic generation, noisy or weak supervision, and rejection sampling
- Experience shaping conversational behaviour over multiple turns
- Experience with alignment-style fine-tuning while preserving general capability
- Experience training multilingual or low-resource-language models from scratch
- Experience with tokenizer design across scripts and typologically diverse languages
- Experience balancing unequal per-language data
- H1-B and STEM OPT candidates are not supported
Benefits
Comp & perks- Flexible, project-based work
- Competitive compensation
- Fully remote role
- Work on your own schedule
- Weekly payments via Stripe or Wise
- Projects can be extended, shortened, or concluded early depending on needs and performance
- Collaboration with leading AI researchers
- Reasonable accommodations upon request