Job Description
We are seeking a visionary Senior AI/ML Engineer to spearhead our next-generation generative AI initiatives. As we look toward the technological landscape of 2024 and beyond, your role will be pivotal in defining how our AI interacts with users and solves complex problems.
In this role, you will work at the intersection of research and production, building robust systems that power our core products. You will have the opportunity to work with cutting-edge Large Language Models (LLMs), computer vision, and predictive analytics. If you are passionate about the future of AI and want to build the foundation for the next decade of technology, we want to hear from you.
Why Join Us?
- Competitive compensation package and equity.
- Work with a world-class team of engineers and researchers.
- Flexible remote-first culture with offices in SF and NYC.
- Focus on ethical AI and responsible deployment.
Responsibilities
- Model Development: Design, train, and fine-tune state-of-the-art machine learning models, with a focus on generative AI and NLP.
- System Architecture: Build scalable, high-performance inference pipelines and MLOps infrastructure to support production workloads.
- Data Engineering: Collaborate with data scientists to curate high-quality datasets and implement data pipelines for continuous model improvement.
- Performance Optimization: Optimize model latency, throughput, and accuracy to meet strict production SLAs.
- Research & Innovation: Stay abreast of the latest research in AI/ML and prototype novel solutions to improve product capabilities.
- Code Review & Mentorship: Conduct code reviews, provide technical mentorship to junior engineers, and contribute to the engineering culture.
Qualifications
- Education: Master’s or Ph.D. in Computer Science, Mathematics, or a related field (or equivalent practical experience).
- Experience: 5+ years of professional experience in machine learning engineering or applied research.
- Programming: Proficiency in Python, PyTorch, TensorFlow, or JAX.
- Infrastructure: Strong experience with cloud platforms (AWS, GCP, or Azure) and containerization technologies (Docker, Kubernetes).
- LLMs: Hands-on experience with fine-tuning LLMs (e.g., GPT, Llama, BERT) and RAG (Retrieval-Augmented Generation) architectures.
- Soft Skills: Excellent problem-solving skills and the ability to communicate complex technical concepts to non-technical stakeholders.