Senior Machine Learning Founding Engineer

About the Client & Project We are representing a high-potential AI product venture backed by a $100M+ initial commitment from a highly profitable global tech group. The company is building next-generation, AI-native workspace and communication software designed to fundamentally transform how millions of users execute daily workflows. Rather than building simple conversational chatbots, the team focuses on Agentic AI - multi-step reasoning, tool integration, and persistent long-term context operating under permissioned autonomy (always keeping the user in control). Their flagship product shifts users from manually processing high-volume communications and daily tasks to simply reviewing and approving AI-completed work. The team operates with an exceptionally high talent density, combining deep AI research with rapid, hands-on production engineering. Why Join This Team? Founding Member Impact: Drive technical architecture, engineering culture, and product strategy from Day 1. Massive Financial Runway: Backed by an initial $100M investment, combining the speed and ownership of a startup with long-term financial stability. True Remote Autonomy: Work in a high-trust environment with full flexibility over your working hours and location. Top-Tier Compensation: Highly competitive Cash + Equity structure. Meaningful Engineering Challenges: Solve non-trivial production issues around model non-determinism, real-world tool execution, and low-latency agent reasoning. Core Focus & Responsibilities Core ML & Agent Systems: Architect and ship production-grade ML systems powering proactive AI agents, task-triage engines, and multi-step reasoning workflows. End-to-End ML Ownership: Take full accountability for the ML lifecycle - data curation, model fine-tuning/training, inference optimization, evaluation, and production monitoring. Agentic Logic & Guardrails: Build reliable systems that interact with external tools and APIs while maintaining high reliability, safety, and persistent contextual memory. Research to Production: Translate state-of-the-art LLM and agentic research into high-availability software handling real-world user workflows. Technical Leadership & Mentorship: Lead design reviews, set high engineering standards, and mentor peers across the ML team. Cross-Functional Ownership: Partner closely with Product, Engineering, and Research leads to translate complex user problems into shipped features. Tech Stack & Key Concepts Primary Language: Python ML Frameworks: PyTorch / JAX Core Paradigms: Agentic Workflows, Function Calling / Tool Integration, Long-Context Memory, Automated Workflow Triage Infrastructure: GPU-accelerated training & inference pipelines, distributed systems, evaluation & observability frameworks Ideal Candidate Profile Production ML Experience: Proven track record of architecting, deploying, and maintaining production AI/ML systems at scale. Systems Engineering Mindset: Strong software design principles (clean code, scalable architecture) with a clear focus on robust systems over one-off scripts. Agentic AI Intuition: Understanding of modern foundation model behavior, failure modes, function calling, and edge-case resolution in live environments. High Ownership & Velocity: Comfortable navigating ambiguity, prioritizing under production constraints (latency, cost, safety), and driving projects independently to completion. Key Outcomes & Expected Impact Delivery of core ML subsystems that consistently meet strict targets for reliability, speed, and cost-efficiency. Scalable data, training, and inference infrastructure designed for long-horizon context and persistent memory. Measurable reduction in manual user effort, delivering a seamless AI-driven product experience. Recruitment Process We value transparency, speed, and efficiency: Initial Screening Call (Recruiter / HR alignment) Technical Deep-Dive (Discussion with Technical Leads / System Design) Hands-on Architectural Session (Practical ML & Agentic systems engineering challenges) Final Culture & Alignment Call (Prompt decision & offer) (Total of 4 stages max, with fast feedback at every step).