Machine Learning Engineer in the Optimization team - US Remote
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Here at Hugging Face, we're on a journey to advance good Machine Learning and make it more accessible. Along the way, we contribute to the development of technology for the better.
We have built the fastest-growing, open-source, library of pre-trained models in the world. With more than 1 Million+ models and 320K+ stars on GitHub, over 15.000 companies are using HF technology in production, including leading AI organizations such as Google, Elastic, Salesforce, Grammarly and NASA.
Transformers in NLP pushed forward the computational requirements and the trend started to expand to other modalities such as Computer Vision and Speech which are actively adopting Transformer architectures to build the latest state-of-the-art models.
About the Role
Hugging Face has become the most popular, community-driven project for training, sharing, and deploying the most advanced machine learning models. Workload efficiency is key to our mission of democratizing state of the art and we are always looking to push the boundaries for faster, and more efficient ways to train and deploy models.
If you like digging into the dark side of low-level system integrations, compiler support, and framework optimizations: we should talk!
We are looking for talented people to join the Hugging Face Special Ops team, focusing on:
This is an exciting opportunity to work at the edge of AI on both model architectures and hardware technologies! As additional material, you may want to take a look at the recent announcement about the collaboration with GraphCore on IPUs to get a better sense of what it means in practice .
About You
You'll enjoy working in this team if you like digging into the dark side of low-level system integrations, compiler support, and framework optimizations. At the intersection of software engineering and machine learning, you would be in charge of integrating the latest features from our hardware partners in Python/C++, designing rich and easy Python APIs in the continuity of what Hugging Face did with transformers, tokenizers, and the datasets library with a strong focus on performances. Linux or embedded devices experience would be a great plus for the job.
More about Hugging Face
We are actively working to build a culture that values diversity, equity, and inclusivity.We are intentionally building a workplace where people feel respected and supported-regardless of who you are or where you come from. We believe this is foundational to building a great company and community. Hugging Face is an equal opportunity employer and we do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.
We value development.You will work with some of the smartest people in our industry. We are an organization that has a bias for impact and is always challenging ourselves to continuously grow. We provide all employees with reimbursement for relevant conferences, training, and education.
We care about your well-being. We offer flexible working hours and remote options. We offer health, dental, and vision benefits for employees and their dependents. We also offer parental leave and flexible paid time off.
We support our employees wherever they are. While we have office spaces in NYC and Paris, we're very distributed and all remote employees have the opportunity to visit our offices. If needed, we'll also outfit your workstation to ensure you succeed.
We want our teammates to be shareholders. All employees have company equity as part of their compensation package. If we succeed in becoming a category-defining platform in machine learning and artificial intelligence, everyone enjoys the upside.
We support the community. We believe major scientific advancements are the result of collaboration across the field. Join a community supporting the ML/AI community.
We have built the fastest-growing, open-source, library of pre-trained models in the world. With more than 1 Million+ models and 320K+ stars on GitHub, over 15.000 companies are using HF technology in production, including leading AI organizations such as Google, Elastic, Salesforce, Grammarly and NASA.
Transformers in NLP pushed forward the computational requirements and the trend started to expand to other modalities such as Computer Vision and Speech which are actively adopting Transformer architectures to build the latest state-of-the-art models.
About the Role
Hugging Face has become the most popular, community-driven project for training, sharing, and deploying the most advanced machine learning models. Workload efficiency is key to our mission of democratizing state of the art and we are always looking to push the boundaries for faster, and more efficient ways to train and deploy models.
If you like digging into the dark side of low-level system integrations, compiler support, and framework optimizations: we should talk!
We are looking for talented people to join the Hugging Face Special Ops team, focusing on:
- Bridging transformers models with state-of-the-art AI hardware
- Ensuring the above models meet the expected performance
- Designing easy to use and proficient Developer Experience for our users
- Deploying these models in the most efficient and scalable way.
This is an exciting opportunity to work at the edge of AI on both model architectures and hardware technologies! As additional material, you may want to take a look at the recent announcement about the collaboration with GraphCore on IPUs to get a better sense of what it means in practice .
About You
You'll enjoy working in this team if you like digging into the dark side of low-level system integrations, compiler support, and framework optimizations. At the intersection of software engineering and machine learning, you would be in charge of integrating the latest features from our hardware partners in Python/C++, designing rich and easy Python APIs in the continuity of what Hugging Face did with transformers, tokenizers, and the datasets library with a strong focus on performances. Linux or embedded devices experience would be a great plus for the job.
More about Hugging Face
We are actively working to build a culture that values diversity, equity, and inclusivity.We are intentionally building a workplace where people feel respected and supported-regardless of who you are or where you come from. We believe this is foundational to building a great company and community. Hugging Face is an equal opportunity employer and we do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.
We value development.You will work with some of the smartest people in our industry. We are an organization that has a bias for impact and is always challenging ourselves to continuously grow. We provide all employees with reimbursement for relevant conferences, training, and education.
We care about your well-being. We offer flexible working hours and remote options. We offer health, dental, and vision benefits for employees and their dependents. We also offer parental leave and flexible paid time off.
We support our employees wherever they are. While we have office spaces in NYC and Paris, we're very distributed and all remote employees have the opportunity to visit our offices. If needed, we'll also outfit your workstation to ensure you succeed.
We want our teammates to be shareholders. All employees have company equity as part of their compensation package. If we succeed in becoming a category-defining platform in machine learning and artificial intelligence, everyone enjoys the upside.
We support the community. We believe major scientific advancements are the result of collaboration across the field. Join a community supporting the ML/AI community.
Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.
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