10 Machine Learning Roles Open at Hugging Face
One of the coolest hubs of AI is looking for researchers and interns to build an open-sourced future. Hugging Face, one of the developer’s favourite platforms on the internet is building a company and community, pushing forth the advancements in AI. The team is currently looking for candidates who love building tools for and collaborating with the wider community and share their vision for making technology accessible. Here are 10 full-time and internships opportunities at Hugging Face for candidates looking to work in a diverse ML environment: Applied Policy Researcher Ever dreamed of influencing both policymakers and developers? This role will let you build tools and participate in policy conversations, bridging the gap between regulations and tech. Your written communications skills will come in handy for this internship. Apply here ML Engineer for Audio In this role, candidates will play a part in improving the accessibility of state-of-the-art speech-to-text and text-to-speech technologies for the open-source community. Prior expertise in the industry, particularly in speech recognition, speaker diarization, dialogue systems, or text-to-speech, is considered beneficial.Successful candidates will actively engage with established open-source libraries, including but not limited to Transformers. Their responsibilities will encompass fortifying the support for resilient speech-to-text, speaker diarization, and text-to-speech within these existing frameworks. Moreover, they will take the lead in conceiving and developing innovative open-source libraries tailored for machine learning applications in the audio domain. Apply here Internships ML Engineer, Watermarking Watermarking has garnered attention throughout the year, driven by an increasing demand to distinguish between content generated by AI and humans. This internship operates at the intersection of language, vision, and audio modalities, with a primary emphasis on imprinting models’ outputs with watermarks. The objective is to upgrade the security of these outputs and facilitate their deployment in suitable contexts. Collaborating closely with ML engineers, the intern will play a crucial role in assimilating research into open-source toolkits. Furthermore, they will actively contribute to the dissemination of these toolkits, ensuring widespread use within the community. Apply here ML Engineer, Generation This internship operates at the corner of software engineering and ML engineering, merging Large Language Model (LLM) research with transformative multimodal generative advancements in transformers, all designed for user-friendly integration. Throughout the internship, participants will gain exposure to the intricacies highlighting LLM Application Programming Interfaces (APIs), including elements such as hardware acceleration, challenges related to numerical precision, common pitfalls in machine learning, and the significance of building scalable software solutions. Apply here ML Engineer, Quantization Quantization for large language models (LLMs) holds promise, enabling the operation and fine-tuning of LLMs on consumer-grade hardware, thereby making the technology more accessible to a more extensive user base. Presently, several academic publications have set the stage for a competitive pursuit of the 1-bit precision transformer model. This internship is dedicated to understanding these quantization techniques. The objective is to replicate select outcomes while bringing together diverse approaches outlined in various academic papers. Apply here ML Engineer, Tokenizers and Maintenance This internship is designed to provide practical exposure to the fundamental upkeep of the Transformers library, coupled with research projects concentrated on investigating tokenizer-less and standardisation methods, exploring their potential integration into the `transformers` architecture. Examples include invictus717/MetaTransformer, improving upon Canine, MegaByte, CharBert, and similar initiatives. Apply here ML Engineer, Computer Vision The selected candidate for this role will collaborate closely with ML engineers throughout the organisation to introduce models, tools, and datasets. Additionally, they will engage in research efforts to enhance the accessibility and practicality of these resources for the broader community. Apply here ML Engineer, Document AI The selected candidates will collaborate with experts in the open-source domain, specialising in ML applications for computer vision and document AI. This includes development and integration of research concepts, along with driving progress in the field through the establishment of benchmarks, leaderboards, implementations, and evaluations. Apply here ML Engineer, Data processing This internship is all about understanding the effectiveness of data fueling large language models today. Collaborating with primary contributors to web-scale datasets, such as Guilherme, the lead author of the RefinedWeb dataset, the focus will be on pushing the boundaries of LLM model performance through data engineering and processing. Apply here ML Engineer, BigCode Chosen candidates will become integral members of the team leading the large-scale collaboration. A knack for community management, coordination of distributed teams, and addressing various tasks within a highly adaptable project framework are essential qualities for applicants. Apply here
The Hugging Face team is looking for candidates who love building tools and collaborating with the AI community
The team is currently looking for candidates who love building tools for and collaborating with the wider community
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["Hugging Face", "machine learning", "TPU", "AI", "ML", "Transformers", "computer vision", "document AI", "Scala", "R"]
["AI", "machine learning", "ML", "computer vision", "Hugging Face", "Transformers", "document AI", "TPU", "R", "Scala"]
Tasmia is a tech journalist at AIM, looking to bring a fresh perspective to emerging technologies and trends in data science, analytics, and artificial intelligence.