What You’ll Do

Collaborate closely with our research and engineering teams on one of the areas:

• Library Development: Architect and enhance open-source Python tooling for alignment, explainability, uncertainty quantification, robustness, and machine unlearning.
• Model Benchmarking: Conduct rigorous evaluations of LLMs and deep networks under domain shifts, adversarial conditions, and regulatory constraints.
• Explainability & Trust: Design and implement XAI techniques (LRP, SHAP, Grad-CAM, Backtrace) across text, image, and tabular modalities.
• Mechanistic Interpretability: Probe internal model representations and circuits—using activation patching, feature visualization, and related methods—to diagnose failure modes and emergent behaviors.
• Uncertainty & Risk: Develop, implement, and benchmark uncertainty estimation methods (Bayesian approaches, ensembles, test-time augmentation) alongside robustness metrics for foundation models.
• Research Contributions: Author and maintain experiment code, run systematic studies, and co-author whitepapers or conference submissions.

General Required Qualifications

• Strong Python expertise: writing clean, modular, and testable code.
• Theoretical foundations: deep understanding of machine learning and deep learning principles with hands-on experience with PyTorch.
• Transformer architectures & fundamentals: comprehensive knowledge of attention mechanisms, positional encodings, tokenization and training objectives in BERT, GPT, LLaMA, T5, MOE, Mamba, etc.
• Version control & CI/CD: Git workflows, packaging, documentation, and collaborative development practices.
• Collaborative mindset: excellent communication, peer code reviews, and agile teamwork.
   

Preferred Domain Expertise (Any one of these is good) :

• Explainability: applied experience with XAI methods such as SHAP, LIME, IG, LRP, DL-Bactrace or Grad-CAM.
• Mechanistic interpretability: familiarity with circuit analysis, activation patching, and feature visualization for neural network introspection.
• Uncertainty estimation: hands-on with Bayesian techniques, ensembles, or test-time augmentation.
• Quantization & pruning: applying model compression to optimize size, latency, and memory footprint.
• LLM Alignment techniques: crafting and evaluating few-shot, zero-shot, and chain-of-thought prompts; experience with RLHF workflows, reward modeling, and human-in-the-loop fine-tuning.
• Post-training adaptation & fine-tuning: practical work with full-model fine-tuning and parameter-efficient methods (LoRA, adapters), instruction tuning, knowledge distillation, and domain-specialization.

Additional Experience (Nice-to-Have)

• Publications: contributions to CVPR, ICLR, ICML, KDD, WWW, WACV, NeurIPS, ACL, NAACL, EMNLP, IJCAI or equivalent research experience.
• Open-source contributions: prior work on AI/ML libraries or tooling.
• Domain exposure: risk-sensitive applications in finance, healthcare, or similar fields.
• Performance optimization: familiarity with large-scale training infrastructures.

What We Offer

• Real-world impact: address high-stakes AI challenges in regulated industries.
• Compute resources: access to GPUs, cloud credits, and proprietary models.
• Competitive stipend: with potential for full-time conversion.
• Authorship opportunities: co-authorship on papers, technical reports, and conference submissions.