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Adapter, based in the United States, is seeking a Machine Learning Engineer to fine-tune transformer-based models and implement real-time pipelines in production. You will collaborate with a brilliant team and tackle innovative consumer use-cases.
The ideal candidate should have 3+ years of experience in ML, strong Python skills, and the ability to work with large datasets. The position allows for both remote and in-person collaboration.
The widespread adoption of intelligent technologies powered by automation, AI, ML, and knowledge graphs is accelerating. As these technologies become increasingly accessible, our aim is to make their capabilities empowering, trustworthy, and useful to real people in the real world.
Adapter was founded in 2022 by Adam Ghetti and Dr. David Bader, with the support of some of the most esteemed Tier 1 Silicon Valley firms and individual entrepreneurs. We are a small but dedicated team, currently working towards solving a significant problem. We recognize the importance of being early movers in this field, and have assembled a well-supported and passionate team to do so.
We have established a culture that promotes both remote work and in‑person collaboration, with team members currently dispersed between Austin, NYC, and the Bay Area. We believe that the integration of these two elements allows for maximum productivity and creativity as we strive to achieve our goal.
Machine Learning Engineer
We are looking for a Machine Learning Engineer who will play a critical role in fine‑tuning transformer‑based models using automation pipelines and implementing real‑time fine‑tuning pipelines in production environments.
You will partner with a brilliant team of designers, engineers, and innovators and will be at the cutting edge of some of the most interesting consumer use‑cases for intelligent technologies.
Full compensation packages are based on candidate experience and certifications.
United States - Remote Pay Range
$180,000 - $225,000 USD