TL;DR

ML Engineer (NLP): Researching, developing, and implementing machine learning models, particularly focusing on language models like GPT-4, and ensuring their seamless integration with clients' applications with an accent on integrating and optimizing ML technologies, natural language processing and understanding. Focus on experimenting with new models and techniques to improve performance and capabilities, and contributing to high coding standards.

Location: 100% remote with a global team. Expect to collaborate with clients in North America, requiring availability during East Coast business hours, potentially extending to West Coast business hours (up to 12 PM PST).

Company

%hirify_global% is a remote-first product design, engineering, and marketing studio, founded in 2012 and based in Boston, that builds AI-powered web and mobile apps for big brands and fast-moving startups.

What you will do

Develop and maintain advanced machine learning models, focusing on natural language processing.
Implement and optimize ML solutions for various client applications.
Work closely with the development team to integrate AI models into existing systems.
Experiment with new models and techniques to improve performance and capabilities.
Participate in code reviews, maintain high coding standards, and contribute to the internal knowledge base.
Stay abreast of the latest developments in AI and machine learning.

Requirements

Strong knowledge of Machine Learning fundamentals, NLP, and LLMs.
Proven experience integrating ML models into data-driven systems.
Proficiency in Python and SQL, with familiarity in ML frameworks (TensorFlow, PyTorch).
Experience with cloud computing services, preferably AWS.
Ability to quickly adapt to new technologies and problem domains.
Fluent in English for effective international collaboration.

Culture & Benefits

100% remote job with a global team.
Paid vacations, sick leave, and holidays.
Flexible working hours (choose when to start, attend scheduled meetings).
Well-being budget and reimbursement for professional development.
Collaborative and innovative work environment with continuous opportunities for learning and growth.

Hiring process

Short interview with HR.
Technical interview with ML Engineer and CTO.
Technical interview with a live coding challenge.
Offer.

Ml Engineer (Nlp)