Machine Learning Engineer

November 29

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AI Fund

WebsiteLinkedInAll Job Openings

deep learning • machine learning • artificial intelligence • startup • entrepreneurship

11 - 50 employees

Founded 2017

🤖 Artificial Intelligence

🤝 B2B

Description

• AI Fund is a venture studio that works with entrepreneurs to build companies rapidly and increase the odds of success. • Backed with $300m from Greylock, NEA, Sequoia and other LPs. • Currently seeking an exceptionally talented Machine Learning Engineer to join our technical team. • Responsible for the end-to-end ownership of scalable machine learning systems, including translating business ideas into actionable solutions. • Build data pipelines, train models, and analyze performance in a production environment. • Join a team that leads development from Proofs of Concept (PoCs) to initial production pipelines in early-stage startups. • Must be adaptable, able to assume multiple roles, and thrive in a dynamic environment.

Requirements

• Minimum of 3 years of professional experience in designing, training, and deploying machine learning models. • Strong foundation in computer science, including data structures, algorithms, and design patterns. • Expertise in Python, demonstrated by implementing multiple medium to large-scale projects. • Proven ability to implement and debug machine learning models. • Familiarity with machine learning frameworks and libraries (e.g., scikit-learn, PyTorch, MLFlow). • Experience in setting up data pipelines. • Experience in developing RESTful APIs. • Industry experience with relational databases, SQL, and NoSQL tools. • Experience with commercial and open-source Large Language Models (LLMs) and frameworks such as LangChain and LlamaIndex. • Education: Bachelor’s degree in Computer Science, Mathematics, or a related field. • Excellent communication skills with the ability to engage in in-depth technical discussions with both engineering teams and business stakeholders. • Proficiency in English. • Self-starter with the ability to work comfortably in an early-stage environment.