Machine Learning Intern/Co-op (Spring/Summer 2026)

About the Role

Cohere is offering a Machine Learning Intern/Co-op position for Spring/Summer 2026. The role involves shipping state-of-the-art models to production, designing and implementing novel research ideas, and building elegant training/deployment pipelines. Interns will join at a pivotal moment, shaping Cohere's products and gaining diverse experience. The recruitment process will involve reviewing applications for suitable matches. Cohere's mission is to scale intelligence to serve humanity by training and deploying frontier models for content generation, semantic search, RAG, and agents. The company values innovation, speed, and diversity. The recruitment process will begin in the upcoming weeks, and we will be carefully reviewing applications and assessing potential candidates for our internships. Should we find a suitable match with your qualifications and our requirements, we will be in touch to discuss the opportunity further and to advance your application to the next stage.

Responsibilities

• Design, train and improve upon cutting-edge models.
• Help us develop new techniques to train and serve models safer, better, and faster.
• Train extremely large-scale models on massive datasets.
• Explore continual and active learning strategies for streaming data.
• Learn from experienced senior machine learning technical staff.
• Work closely with product teams to develop solutions.

Requirements

• To be eligible for this position you should be a student currently enrolled in a post-secondary program, available for a full-time 3-6 month internship, co-op, or research work term.
• Proficiency in Python and related ML frameworks such as Tensorflow, TF-Serving, JAX, and XLA/MLIR.
• Experience using large-scale distributed training strategies.
• Familiarity with autoregressive sequence models, such as Transformers.
• Strong communication and problem-solving skills.
• A demonstrated passion for applied NLP models and products.

Qualifications

• Experience writing kernels for GPUs using CUDA.
• Experience training on TPUs.
• Papers at top-tier venues (such as NeurIPS, ICML, ICLR, AIStats, MLSys, JMLR, AAAI, Nature, COLING, ACL, EMNLP).