Machine Learning Scientist II - Gen AI

About SimpliSafe SimpliSafe is a leading innovator in the home security industry, dedicated to making every home a safe home. With a mission to provide accessible and comprehensive security solutions, we design and build user-centric products that empower individuals and families to protect what matters most. We believe in a collaborative and agile environment where learning and growth are continuous. Our teams are composed of talented individuals who are passionate about technology, security, and delivering exceptional customer experiences. We’re embracing a hybrid work model that enables our teams to split their time between office and home. Hybrid for us means we expect our teams to come together in our state-of-the-art office on two core days, typically Tuesday, Wednesday, or Thursday – working together in person and choosing where they work for the remainder of the week. We all benefit from flexibility and get to use the best of both worlds to get our work done. Why are we hiring? Well, we’re growing and thriving. So, we need smart, talented, and humble people who share our values to join us as we disrupt the home security space and relentlessly pursue our mission of keeping Every Home Secure. About the Role We are seeking a highly motivated and experienced Machine Learning Scientist to join our growing Engineering team. As a key contributor, you will play a crucial role in developing and implementing cutting-edge machine learning models, with a focus on Computer Vision (CV), Multi-modal Large Language Models (LLMs) and Agentic AI to enhance our products and services. Responsibilities: Develop and fine-tune large language models (LLMs) and vision-language models (VLMs) to address real-world challenges in the home security space Work with key stakeholders to identify key research initiatives that can have impact on business outcomes. Take research initiatives from idea generation to production. Collaborate with engineers and product managers to integrate capabilities into our existing systems. Stay up-to-date on the latest advancements in LLMs, VLMs, and multimodal systems. Evaluate new techniques for potential adoption and improvement of internal capabilities. Qualifications: MS or PhD in Computer Science, Artificial Intelligence, or a related field. Experience training or fine-tuning large language models (LLMs) using modern frameworks. Strong grasp of deep learning, particularly transformer architectures and foundational model training techniques for text and vision modalities. Proficient in Python and relevant ML libraries (e.g., PyTorch, TensorFlow, HuggingFace Transformers). Hands-on experience in developing and deploying LLM- or VLM-powered applications. Familiarity with prompt engineering, retrieval-augmented generation (RAG), MCP (Model Context Protocol, Agentic AI and evaluation of generative models. Understanding of MLOps practices and how to scale experiments into production-grade solutions. Strong communication and documentation skills. Collaborative mindset with the ability to thrive in a fast-paced, interdisciplinary environment. Bonus Points: Experience with speech recognition, natural language understanding, or natural language generation. Publication in posters, conference proceedings, journal articles, workshops etc. Familiarity with cloud computing platforms (e.g., AWS, GCP). Contributions to open-source NLP, LLM or CV projects. What Values You’ll Share Customer Obsessed - Building deep empathy for our customers, putting them at the core of our work, and developing strong, long-term relationships with them. Aim High - Always challenging ourselves and others to raise the bar. No Ego - Maintaining a “no job too small” attitude, and an open, inclusive and humble style. One Team - Taking a highly collaborative approach to achieving success. Lift As We Climb - Investing in developing others and helping others around us succeed. Lean Nimble - Working with agility and efficie ... (truncated, view full listing at source)