• American Start Up
• Permanent contract

American start up based in the USA focus in personal assistant market

-Design groundbreaking AI solutions and develop prototypes that drive the future of Duckbill's user-facing experience.

- Leverage the latest innovations in AI and ML to provide a hyper-personalized experience to our users, including the development of high-engagement recommendation systems.

• Build tools to maximize the output and quality of our human personal assistants (copilots), reimagining the collaboration between humans and machines in the virtual assistant space.


• Automate task execution by leveraging the entire AI stack, from traditional ML to the latest LLM-agent frameworks.


• Improve human task execution quality by enhancing our intelligent information retrieval systems, utilizing search and RAG techniques.


• Contribute to our operations team's excellence by building smart monitoring and intervention systems.


• Enhance our AI ops stack by designing and improving our evaluation frameworks, allowing the team to iterate on prompts, models, and approaches with both agility and thoroughness.


• Enhance our human annotation flow stack to enable leveraging our human resources for annotation, model comparison, and golden dataset generation.


• Maximize the value extracted from our proprietary data using state-of-the-art techniques (fine-tuning, RLHF, RAG, etc.), critically contributing to the defensibility of our AI strategy.

Salary depends on the experience between 50.000-70.000€

Remote work 3/4 days per week

• 4+ years of experience in an ML engineering role.


• Proficiency in machine learning, with experience in areas such as Generalized Linear Models, Gradient Boosting, Deep Learning, and Probabilistic Modeling. Specialized knowledge in recommender systems is a plus.


• Strong engineering skills in Python and data manipulation skills in SQL.


• Solid knowledge of the main patterns in ML model deployment, both online (API-based) and offline (batch jobs), with the ability to design production-grade solutions.


• Experience with the entire MLOps stack, especially in experiment/model performance tracking and model monitoring in production.


• Strong product and business intuition, with a deep understanding of what makes an ML project a business success.


• Interest and foundational knowledge in the LLM space.