Machine Learning Engineer

Required Qualifications

• Proven Production ML Experience: 6+ years building and scaling machine learning systems in production environments, with hands-on experience moving from experimentation to customer-facing deployments
• Deep Neural Networks Foundation: Strong background in classical neural networks and deep learning fundamentals before specializing in modern LLMs and transformer architectures – you understand the foundations, not just the latest tools
• Product-Focused ML Mindset: Experience building ML systems that solve real business problems, with a track record of integrating classification, prediction, or recommendation systems into actual products customers use
• Multi-Company Perspective: Experience across multiple organizations (scale-ups, startups, or combination), giving you practical knowledge of what tools to build vs buy and how to avoid over-engineering
• Technical Versatility: Strong Python skills with flexibility across ML frameworks and tools – comfortable adapting to our stack including LangChain, evaluation frameworks, and workflow orchestration tools like Temporal
• Self-Directed Leadership: Ability to operate autonomously while maintaining close alignment with leadership, comfortable with frequent check-ins but capable of driving projects independently
• Cross-Functional Collaboration: Experience working closely with product teams and potentially customers, translating technical capabilities into business value and user experiences
• Nice to Haves: Experience with AI agents, LLMs, or modern generative AI applications Cybersecurity domain knowledge or experience applying ML to security challenges Background at ML-first companies or organizations where ML was core to the product Experience with modern MLOps practices and cloud-based ML infrastructure Track record of optimizing model performance and controlling AI system costs
• Experience with AI agents, LLMs, or modern generative AI applications
• Cybersecurity domain knowledge or experience applying ML to security challenges
• Background at ML-first companies or organizations where ML was core to the product
• Experience with modern MLOps practices and cloud-based ML infrastructure
• Track record of optimizing model performance and controlling AI system costs