ARC-Net | Applied Research Capability Network

About the Company and Project ARC-Net supports deep-tech projects in taking the critical 0-to-1 step, specializing in foundational research and applied AI. Incubated at ARTPARK, Indian Institute of Science (IISc) , ARC-Net provides scientific, technical, and engineering capability for emerging technologies. You will be working on MyPraX , a cognitive-AI platform that models how people perceive and interpret advertising to predict real brand uplift. The platform integrates multimodal video analysis, neuro-semantic token mapping, Bayesian inference, and temporal cognition modelling. MyPraX is currently building its first working demo and v0 prototype, which will become the foundation of a full-fledged product for creative pre-testing and brand development. Role Description We are hiring a Machine Learning Engineer to design and build the core ML pipeline powering the MyPraX platform. You will work across multimodal data processing, probabilistic modelling, backend ML services, and prototype system design. You will help shape the technical foundation of MyPraX as it evolves from prototype to product. Contract: Project-based (with potential long-term extension) Compensation: ₹40,000/month (with potential increase as the project scales) Location: On-site, Bengaluru Start Date: Immediate Responsibilities Build and maintain the end-to-end ML pipeline for MyPraX’s demo and early versions. Process multimodal outputs (video, audio, semantic signals) from existing analysis tools. Implement uplift-prediction logic using Bayesian/probabilistic modelling. Design clean, lightweight APIs (FastAPI/Flask) for integration with the platform dashboard. Create synthetic and human-sample testing workflows for experiments. Iterate rapidly on prototype systems while keeping long-term architecture in mind. Prepare structured outputs and visualisable data for demos and experiments. Qualifications Required: Strong Python programming skills. Experience with ML engineering and multimodal pipelines (CV, audio). Hands-on experience with Bayesian or probabilistic modelling. Ability to work with temporal or sequence-level data. Familiarity with FastAPI/Flask or equivalent backend frameworks. Proficiency in debugging, data processing (NumPy, Pandas), and prototype development. Comfortable working independently in fast-moving research environments. Nice to Have: Experience in cognitive science, behavioural modelling, or causal inference. Prior exposure to deep-tech, AI-based research, early-stage product development, or LLM-based evaluation. Background in designing data workflows or experiment systems.