About Zuma
Zuma makes an automated sales agent that converses with 100% of inbound leads, ultimately improving the way consumers interact with businesses and organizations. We’ve built this from the ground up using AI, ML, and human support which helps increase sales conversion and support capacity for businesses of all kinds. Zuma is one of the fastest-growing startups in San Francisco, and is well-funded and backed by world-class investors such as Andreessen Horowitz (a16z), Y-Combinator, Joe Montana’s fund (Liquid 2 Ventures), Day One Ventures, Soma Capital, and other notable angel investors including Austen Allred (from Lambda School), YC’s former-COO Qasar Younis, among others.
Headquartered in San Francisco, USA, we operate nationally and have plans to grow rapidly over the next few years. To do that, we need great people committed to our vision in a big way. We're looking to build a team of rockstars that are equally excited about the opportunity to leverage technology to improve the way customers interact with businesses!
**Job Description: **
We are seeking a highly skilled and motivated Machine Learning Engineer to join our team with a focus on Natural Language Processing (NLP), Generative AI, and Reinforcement Learning from Human Feedback (RLHF). In this role, you will play a critical role in developing state-of-the-art AI technologies that contribute to an expansive suite of tools designed to enhance our Zuma suite.
**Responsibilities: **
1. Design, develop, and evaluate AI systems and products, utilizing your expertise in NLP, Generative AI, RLHF, and general Machine Learning.
2. Collaborate with cross-functional teams, including Product, Design, and Engineering, to understand requirements and devise AI solutions.
3. Develop and refine our suite of AI-driven tools, which may include aspects of conversational AI, intent recognition, and other NLP components.
**Qualifications: **
3+ Years Experience with:
1. Proven experience in RLHF, Natural Language Processing, Conversational AI, and general Machine Learning principles.
2. Proficiency in Python and familiarity with machine learning frameworks such as TensorFlow, PyTorch, or similar.
3. Demonstrable track record of ML model development, including problem definition, data gathering, model selection, validation, and deployment.
4. Experience with deep learning architectures such as transformers
5. Familiarity with modern software development methodologies, with proficient use of version control systems like Git.
6. Excellent problem-solving abilities, attention to detail, and capacity to work both independently and in a collaborative team environment.
7. Exceptional written and verbal communication skills.