About the Client & ProjectWe 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 & ResponsibilitiesCore 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 ConceptsPrimary Language: PythonML Frameworks: PyTorch / JAXCore Paradigms: Agentic Workflows, Function Calling / Tool Integration, Long-Context Memory, Automated Workflow TriageInfrastructure: GPU-accelerated training & inference pipelines, distributed systems, evaluation & observability frameworksIdeal Candidate ProfileProduction 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 ImpactDelivery 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 ProcessWe 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).
Yard Corporate is a global cybersecurity startup that focuses on revolutionizing security data management through a scientific approach utilizing AI and streaming data. The company aims to transform how organizations handle security data by separating it from compliance, which allows for real-time threat detection at reduced costs. Yard Corporate serves a clientele that includes Fortune 500 companies across the financial, healthcare, and insurance sectors. As an early-stage startup, it offers significant opportunities for foundational engineering and innovation in the cybersecurity industry.
