We strongly believe that intertwining the novelty of research with the robustness of engineering yields best results when it comes to emerging technologies, such as generative AI. As a Research Engineer, you will play a pivotal role in shaping and refining large-scale ML systems, ensuring they are not only advanced but also steerable and trustworthy. Our mission draws inspiration from integrating state-of-the-art models with human values, understanding the depths of neural networks, and developing innovative tools and methods. With us, every code you write, every experiment you conduct, is a testament to our combined ambition of shaping the future of AI responsibly.
- Delve deep into the core of our infrastructure and codebase, from enhancing efficiency and throughput to orchestrating scientific evaluations.
- Lay the foundation and continually refine expansive ML systems, ensuring their reliability and efficiency.
- Participate actively in research discussions, understanding the broader picture while being involved in intricate details, aiming to maximize the impact of your insights.
- Identify, evaluate, and record optimal methods for a broad spectrum of tasks that resonate with our user base.
Ideal candidate skills and attributes
- Has 5+ years of experience working in the data related domain, as a data scientist or a machine learning engineer.
- Has at least MS in the representative fields.
- Boasts substantial expertise in software engineering, with a focus on high-performance, expansive ML frameworks.
- Demonstrates the ability to articulate the motivations behind your work and can convey complex technical concepts.
- Prioritizes results, leaning towards adaptability and making impactful decisions.
- Has a keen interest in expanding knowledge about machine learning research, with prior experience in projects where you played a significant role.
- Enjoys collaborative work, believes in data-centric decision-making, and is open to both teaching and learning.
- Familiar with technologies like GPUs, Kubernetes, PyTorch, and OS fundamentals, and transformer-based language modeling.
- Understands the societal ramifications of AI advancements and works with a sense of responsibility and ethical considerations.
- Is proactive, adaptable, and eager to immerse in diverse tasks, even if they stretch beyond the conventional role.
- Possesses a broad understanding of the architecture and mechanics of large language models.
- Transforms vague challenges into distinct issues, drawing on fundamental tenets that are universally applicable.
- Maintains a current perspective, fueled by a genuine curiosity in nascent research and sectoral evolutions.
- Specialized experience in any of the following domains: NLP, computer vision, GANs or stable diffusion.
Consider submitting projects that demonstrate the following aspects:
- Innovative methods for analyzing language models for potential detrimental behaviors.
- Innovative approaches to prompt chaining, novel prompting techniques and autonomous agent construction.
- Curation of evaluation databases for novel capabilities in language models, such as persuasion or deception.
- Enhancement of the efficiency of novel attention mechanisms and compare computational aspects of different Transformer variations.
- Structuring datasets for optimal model consumption.
- Augmentation of distributed training systems to harness the power of thousands of GPUs, ensuring fault tolerance.
- Interactive tools’ design to visualize the behavior in language models, especially in large-scale distributed systems.