Stack AI
AI/ML Engineer
Stack AI, Boston, New York, United States,
About Stack AI
Vision : Democratize access to Large Language Models, so that anyone can build AI-powered applications that generate positive impact to the world.
Product : A user-friendly and intuitive No-Code platform integrating best AI models (OpenAI, Anthropic, Google, open source), most common data sources (OneDrive, Google Drive, Airtable, Notion) and SaaS (Zapier, HubSpot, etc.). We do the heavy-lifting for you, so you can focus on the product and business.
Traction : Launched 8 months ago; over 35,000 users and 300+ paying customers (including public companies and unicorns!).
Team : Founded by two MIT PhDs, backed by major investors such as Y Combinator and Gradient Ventures, we are now a four-engineer team.
Values : Default to action, radical transparency, boldness, and strong opinions loosely-held.
Responsibilities:
We need 10X engineers to make this happen!
True wizards
that can deliver on a daily basis in the high-paced environment.
In this role, you will...
Enhance our retrieval-augmented generation (RAG) capabilities, ensuring our models effectively integrate and utilize diverse data sources.
Work on embedding generation techniques, optimizing them for accuracy and efficiency.
Keep abreast of the latest developments in AI models, their evolving capabilities, and the complexities of integration. Position yourself as the go-to expert within the team, guiding our end users in crafting their use cases with the most suitable models.
Collaborate with backend and frontend teams to integrate AI models into products and services.
We're all about our users - listen to their needs, help them, build strong relationships! Expect to dive into customer support now and then as everyone else in the team.
Required Skills and Qualifications:
You will be a
great
fit if you have...
Bachelor's or Master's degree in Computer Science, Machine Learning, AI, or related fields.
Proven experience in machine learning and AI, with a focus on generative models.
Knowledge of retrieval-augmented generation pipelines and their implementation.
Strong proficiency in Python and popular machine learning frameworks like TensorFlow or PyTorch.
Experience with embedding techniques and understanding of their applications in AI models.
Excellent problem-solving skills and the ability to work in fast-paced environments.
Strong communication and collaboration skills.
Preferred Qualifications:
You will be an
exceptional
fit if you
also
have...
PhD in a relevant field.
Published research or projects in the field of generative AI, embeddings, or retrieval-augmented generation.
Experience with large-scale ML deployments in a production environment.
Contribution to open-source AI/ML projects.
In-depth knowledge of generative AI hardware, including an understanding of GPU architectures, requirements, and optimization techniques for AI model deployment.
Beyond these skills, what we value the most is your ability to remain curious and open to learning new skills. As a early-stage startup joiner, you may need to wear many hats.
#J-18808-Ljbffr
Vision : Democratize access to Large Language Models, so that anyone can build AI-powered applications that generate positive impact to the world.
Product : A user-friendly and intuitive No-Code platform integrating best AI models (OpenAI, Anthropic, Google, open source), most common data sources (OneDrive, Google Drive, Airtable, Notion) and SaaS (Zapier, HubSpot, etc.). We do the heavy-lifting for you, so you can focus on the product and business.
Traction : Launched 8 months ago; over 35,000 users and 300+ paying customers (including public companies and unicorns!).
Team : Founded by two MIT PhDs, backed by major investors such as Y Combinator and Gradient Ventures, we are now a four-engineer team.
Values : Default to action, radical transparency, boldness, and strong opinions loosely-held.
Responsibilities:
We need 10X engineers to make this happen!
True wizards
that can deliver on a daily basis in the high-paced environment.
In this role, you will...
Enhance our retrieval-augmented generation (RAG) capabilities, ensuring our models effectively integrate and utilize diverse data sources.
Work on embedding generation techniques, optimizing them for accuracy and efficiency.
Keep abreast of the latest developments in AI models, their evolving capabilities, and the complexities of integration. Position yourself as the go-to expert within the team, guiding our end users in crafting their use cases with the most suitable models.
Collaborate with backend and frontend teams to integrate AI models into products and services.
We're all about our users - listen to their needs, help them, build strong relationships! Expect to dive into customer support now and then as everyone else in the team.
Required Skills and Qualifications:
You will be a
great
fit if you have...
Bachelor's or Master's degree in Computer Science, Machine Learning, AI, or related fields.
Proven experience in machine learning and AI, with a focus on generative models.
Knowledge of retrieval-augmented generation pipelines and their implementation.
Strong proficiency in Python and popular machine learning frameworks like TensorFlow or PyTorch.
Experience with embedding techniques and understanding of their applications in AI models.
Excellent problem-solving skills and the ability to work in fast-paced environments.
Strong communication and collaboration skills.
Preferred Qualifications:
You will be an
exceptional
fit if you
also
have...
PhD in a relevant field.
Published research or projects in the field of generative AI, embeddings, or retrieval-augmented generation.
Experience with large-scale ML deployments in a production environment.
Contribution to open-source AI/ML projects.
In-depth knowledge of generative AI hardware, including an understanding of GPU architectures, requirements, and optimization techniques for AI model deployment.
Beyond these skills, what we value the most is your ability to remain curious and open to learning new skills. As a early-stage startup joiner, you may need to wear many hats.
#J-18808-Ljbffr