Machine Learning Engineer (Audio & LLM Stack)

About Slash Slash is a hi-tech startup studio with a mission to build tech AI-powered products and scalable digital platforms that create real-world impact. Since 2016, we’ve partnered with ambitious enterprises and government organizations to design, engineer, and launch cutting-edge solutions — with Generative AI at the core of what we do. We specialize in AI-powered application delivery, from product design and high-performance engineering to DevOps and AI operations. Headquartered in Singapore, our global team and clients operate R&D hubs across Southeast Asia. We are a team of entrepreneurs, engineers, and product builders dedicated to solving complex technical challenges and turning bold ideas into impactful technology. About our Client Our client’s product is an AI voice-sensing device, a breakthrough wearable that detects the gap between what someone says and how their voice actually sounds. Rooted in Pythagorean acoustic physics and the Navarasa framework, the system functions as a state detector rather than a conventional emotion labeler. The team is a small, fast-moving team building at the intersection of emotion labeling, ancient wisdom, and measurable science. About the Role We are looking for a hands-on Machine Learning Engineer to take complete ownership of our full AI stack. Your primary responsibility will be expanding our speech emotion recognition (SER) model into a physics-based harmonic vocal state engine, alongside building and maintaining our dual-instance production LLM infrastructure. In this role, you will work directly with the Founder and Lead Developer with zero bureaucracy or committee oversight. We need an engineer who excels at owning problems end-to-end. Key Responsibilities 1. Harmonic Vocal State Engine (Audio & Physics) Extend our inherited 30-class speech emotion recognition (SER) model, dataset, checkpoints, and pipeline into a physics-based harmonic detection system using Fourier-derived acoustic analysis and the Navarasa framework. Design and implement an in-house model validation methodology from scratch using approaches like Gemini's emotion labeling API, cross-validation on open datasets (IEMOCAP, RAVDESS), or custom ground-truth evaluation pipelines. 2. LLM Infrastructure & Operations Deploy and manage a dual-instance vLLM setup on GCP (g2-standard-24 instance): GPU 0: Llama 3.1 8B for fast-lane prompts; GPU 1: Qwen 2.5 32B for reasoning-heavy prompts. Own prompt engineering, output validation, Pydantic schema enforcement, retry logic, and quality monitoring across 42 production prompts. Handle infrastructure scaling and migrations independently without reliance on third-party vendors. 3. Model Quality & Continuous Improvement Build evaluation pipelines to detect and catch regressions before users experience them. Continuously optimize and refine model performance as real-world audio accumulates from our device. Requirements Requirements & Qualifications Audio Signal Processing: Strong proficiency in…