Machine Learning Engineer

<p><strong>Our story:</strong></p> <p>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. </p> <p>Adapter was founded in 2022 by <a href="https://www.linkedin.com/in/adam-ghetti">Adam Ghetti</a> and <a href="https://davidbader.net/">Dr. David Bader</a>, 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.</p> <p><strong>What we are looking for:</strong></p> <p>We are looking for a <strong>Machine Learning Engineer </strong>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.</p> <p>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. </p> <p>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.</p> <p><strong>Responsibilities: </strong></p> <ul> <li>Use the latest cutting edge technologies such as LLMS, multimodal models  to handle complex problems.</li> <li>Work with large datasets, perform data preprocessing, and engineer relevant features to enhance model performance.</li> <li>Build frameworks that allow us to iterate and evaluate model versions (ranking, accuracy, latency).</li> <li>Deploy Models at Scale: Collaborate with software engineers to deploy machine learning models into production, ensuring seamless integration with existing systems.</li> <li>Monitoring and Maintenance: Implement monitoring solutions to track model performance in real-time and perform regular maintenance and updates as needed.</li> <li>Collaboration: Work closely with cross-functional teams, including data scientists, software developers, and business analysts, to understand requirements and deliver impactful solutions.</li> <li>Research and Innovation: Stay abreast of the latest advancements in machine learning and contribute to the research and development of innovativ