About the Role
System Two Security is looking to hire a Principal LLM Application Engineer where you will focus on leveraging and optimizing Large Language Models (LLMs) along with the implementation of advanced AI technologies. You will be working on cutting edge projects, and will utilize skills in GenAI, LLM and ML Ops to bring a better experience to customers. You will have the opportunity to be a technical leader and to take ownership for projects. It offers a significant opportunity for professional growth at the forefront of AI in the dynamic field of cybersecurity.
Requirements
Responsibilities
Development and Optimization of LLMs:Â Implement and fine-tune state-of-the-art Large Language Models for various applications, focusing on performance and accuracy.
Evaluating Model Performance:Â Conduct rigorous evaluations of LLMs, assessing effectiveness, efficiency, and business alignment.
Integration of Advanced AI Technologies:Â Implement Retrieval-Augmented Generation (RAG), function calling, and code interpreter technologies to enhance the capabilities of Large Language Models.
Research and Development:Â Stay abreast of the latest advancements in machine learning, particularly in LLMs, LLM agents, and large-scale neural network training.
Data and Model Parallel Training:Â Utilize data and model parallel training techniques for efficient handling of large-scale models.
Cross-Functional Collaboration and Leadership:Â Work with ML engineers, data scientists, and product teams, providing guidance and mentorship.
Documentation and Reporting:Â Maintain detailed documentation of methodologies, models, and results, communicating findings across the organization.
Contribute to product roadmap and vision
Implement and evaluate various LLM application logic ( flows ) and prompting strategies and stay up to date with the latest advancements in this field
Lead the incubation of new initiatives, architect scalable solutions, and drive strategic technology choices to develop and deliver AI/ML capabilities in a micoservcies architecture for our customers.
Design, test, and deploy Machine learning models, including large-language models and build pipelines at scale for batch and real-time use cases.
Required Skills
Bachelor's degree in Computer Science, Engineering, or related field.
5+ years of experience in natural language processing, machine learning, and/or data science.
Experience in Python or R.
Experience working with large language models, such as GPT-3+, LLAMA, or similar.
Strong problem-solving skills and the ability to think creatively to identify new opportunities for LLMs in our products and services.
Experience with one or more deep learning frameworks.
A deep theoretical or empirical understanding of deep learning.
Experience in building, testing and deploying machine learning models, including large language models.
Strong analytical and debugging skills.
Familiarity with cloud-based infrastructure and distributed computing.
Experience with DevOps/MLOps/LLMOps.
Ability to address complex challenges in model training and optimization.
Effective communication skills for conveying technical concepts and collaborating with cross-functional teams.
Passion for staying updated with the latest trends in AI and machine learning.
Preferred Skills
Master's or PhD in Computer Science, AI, or related fields, with a focus on machine learning and natural language processing.
Proficiency in programming languages such as Python or R.
Working experience within Cyber Security.
Experience in building GenAI solutions using RAG framework and building LLM Agentic applications is preferred.
Benefits
Medical, Dental, and Vision
Equity in the company
About the Company
System Two Security is driving SOC transformation with its unique application of AI computing with an initial focus on generative AI powered proactive threat research, threat analysis and iterative threat hunting. The product’s purpose-built language agents respond to new threat actors and attack patterns within minutes with its agentic workflows delivering end-to-end threat detection and containment from integrated feeds of raw cyber threat advisories. Early users of the product include prominent MSSPs, and enterprise SOCs across retail, healthcare, SaaS and technology verticals. It is based in Palo Alto, CA and is venture funded by The Hive.