The Lexsi Labs AI Internship is an outstanding opportunity for students and early-career professionals to gain hands-on experience in cutting-edge AI research and development. This full-time remote internship is ideal for candidates passionate about machine learning, deep learning, large language models (LLMs), explainable AI (XAI), and model interpretability.
As an AI Research Intern, you will collaborate with research and engineering teams on large-scale industry problems, contributing to the development of aligned, interpretable, and safe AI systems. The internship offers exposure to real-world AI challenges while allowing interns to innovate in a startup-meets-research environment focused on speed, reliability, and scalability.
About Lexsi Labs
Lexsi Labs is a frontier lab dedicated to building aligned, interpretable, and safe Superintelligence. Their mission is to create AI tools that empower researchers, engineers, and organizations to unlock AI’s full potential while maintaining transparency and safety. Lexsi Labs emphasizes a flat organizational structure, where every team member contributes hands-on to research innovation. Interns at Lexsi Labs are exposed to industry-scale AI problems, allowing them to build expertise in machine learning, LLMs, explainability, and uncertainty estimation.
Role Overview
The AI Internship at Lexsi Labs focuses on research and development, with a primary emphasis on large-scale AI systems, LLM evaluation, and model interpretability. Interns will work in one or more of the following areas:
- Library development for alignment, explainability, and robustness
- Model benchmarking and evaluation under adversarial or domain-shift conditions
- Mechanistic interpretability and probing internal model circuits
- Uncertainty estimation using Bayesian methods, ensembles, or test-time augmentation
- Explainability techniques such as LRP, SHAP, Grad-CAM, or Backtrace
Interns will gain experience in writing clean, modular Python code, implementing PyTorch models, and contributing to experiments, whitepapers, or conference submissions.
Key Responsibilities
- Collaborate closely with research and engineering teams to develop AI tools and methods
- Architect and enhance Python libraries for model alignment, explainability, and robustness
- Conduct rigorous evaluations of LLMs and deep networks under varying conditions
- Design and implement XAI techniques across text, image, and tabular data
- Probe internal model representations to diagnose failure modes and emergent behaviors
- Develop and benchmark uncertainty estimation and robustness metrics
- Maintain experiment code and document results systematically
- Contribute to research publications or technical reports
Who Can Apply
| Criteria | Details |
|---|---|
| Education | Currently pursuing or completed Master’s/Ph.D. in Machine Learning, AI, Data Science, or related fields |
| Location | Remote (based in Mumbai or open for remote work in India) |
| Duration | Full-Time Internship (6 months) with potential extension or full-time offer |
| Skills | Python, PyTorch, Transformers, LLMs, Machine Learning, Deep Learning, XAI, Mechanistic Interpretability |
| Experience | Academic or research experience in AI/ML, model benchmarking, LLMs, or related projects |
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Required Skills
- Strong Python expertise for modular and testable code
- Solid theoretical understanding of machine learning and deep learning principles
- Hands-on experience with PyTorch and Transformer architectures (BERT, GPT, LLaMA, T5, etc.)
- Familiarity with attention mechanisms, positional encodings, tokenization, and training objectives
- Version control and collaborative workflows using Git and CI/CD pipelines
- Excellent communication, peer code review, and agile collaboration skills
Preferred Domain Expertise
- Explainable AI (SHAP, LIME, IG, LRP, Grad-CAM)
- Mechanistic interpretability: circuit analysis, activation patching, feature visualization
- Uncertainty estimation: Bayesian methods, ensembles, test-time augmentation
- Model quantization, pruning, and parameter-efficient fine-tuning (LoRA, adapters)
- LLM alignment techniques: RLHF workflows, reward modeling, human-in-the-loop fine-tuning
- Post-training adaptation, instruction tuning, and domain-specialized models
Additional Experience (Nice-to-Have)
- Publications in CVPR, ICLR, ICML, KDD, NeurIPS, ACL, or similar conferences
- Contributions to open-source AI/ML libraries
- Experience in risk-sensitive applications like healthcare or finance
- Familiarity with large-scale training infrastructures and performance optimization
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What You Will Gain
- Exposure to cutting-edge AI research in supervised, unsupervised, and LLM-based systems
- Hands-on experience with Python, PyTorch, Transformers, and state-of-the-art AI tools
- Opportunities to contribute to research publications, whitepapers, or conference submissions
- Access to GPU resources, cloud credits, and proprietary models
- Real-world experience solving high-stakes AI problems in regulated industries
- Networking and mentorship from leading AI researchers and engineers
How to Apply
To apply for the Lexsi Labs AI Internship, click the Apply Now button and submit your resume highlighting Python, PyTorch, and AI/ML project experience. Include any research publications, open-source contributions, or prior work with LLMs, XAI, or mechanistic interpretability.
Conclusion
The Lexsi Labs AI Internship is an exceptional opportunity for students and researchers to gain hands-on experience in AI research, model interpretability, and LLM development. Interns will work on large-scale, industry-relevant AI problems while collaborating with top-tier researchers in a fast-paced, innovation-driven environment. If you are passionate about advancing AI safely and effectively while contributing to meaningful research, this internship offers the perfect platform to launch your career in AI and machine learning. 🤖📊💡
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