Are you eager to be at the forefront of large-scale ML systems, blending technical prowess with safety considerations? Dive into a role that combines the mastery of cutting-edge ML technologies and the importance of user trust. As a Software Engineer specializing in Generative AI, you'll be at the helm of constructing advanced systems while ensuring they're robust and aligned with our principles.
We drive policy development and ethics discussions through our core AI team where software engineers specializing in generative AI augment the policy-making process and ethics discussion.
- Architect and optimize large-scale ML systems from conceptualization to deployment.
- Design monitoring systems to keep track of model behaviors, focusing on early detection and prevention of misuse.
- Dive deep into code and infrastructure enhancements, streamlining efficiency, and bolstering reliability.
- Build advanced abuse detection mechanisms and provide insights to research teams for model improvements.
- Engage in the design and execution of scientific experiments, staying in sync with the broader research context.
- Collaborate with teams to create internal tools that streamline operations and improve the user experience.
- Participate actively in research and ethics discussions, understanding the broader picture while being involved in intricate details, aiming to maximize the impact of your insights.
Ideal candidate skills and attributes
- Bachelor’s degree in Computer Science, Software Engineering, or equivalent experience.
- Over 3 years in roles like data science, software engineering, with a touch of security or abuse detection.
- Fluent in SQL, Python, and adept with data analysis tools.
- An appetite to engage with machine learning research and its broader implications.
- Strong knack for articulating complex concepts and a spirit of collaboration.
- Possesses a broad understanding of the architecture and mechanics of large language models.
- Worked with prompt engineering current LLMs, as well as image generation algorithms such as stable diffusion.
- Maintains a current perspective, fueled by a genuine curiosity in nascent research and sectoral evolutions.
Strong candidate experience
- Proven expertise in crafting and scaling ML systems, with familiarity in tools like GPUs, Kubernetes, and PyTorch.
- Experience with machine learning frameworks such as Scikit-Learn, Tensorflow, or Pytorch.
- Exposure to language modeling, especially with transformers, and reinforcement learning.
- Tackled challenges related to prompt engineering and adversarial inputs.
- Designed or been part of full-stack engineering projects, especially centered on internal tool creation.
- Specialized experience in NLP.
Consider submitting projects that demonstrate the following aspects:
- Innovative approaches to prompt chaining, novel prompting techniques and autonomous agent construction.
- Analyzing and contrasting the computational efficiency of different Transformer models.
- Crafting extensive datasets to be easily consumed by models.
- Launching distributed training systems across large GPU networks.
- Conceptualizing strategies for ensuring system robustness and fault tolerance.
- Creating visualization tools that shed light on model dynamics and interactions.
- Writing essays or papers on ethics, policy and implications of AI integration and development.