Senior Machine Learning Founding Engineer

YARD Corporate; United States; Remote; USD 180,000 - 240,000; Full time

Job summary

YARD Corporate is seeking a senior ML engineer to architect production-grade ML systems powering proactive agents and multi-step reasoning workflows. You will own the ML lifecycle from data curation through deployment, optimising inference and monitoring in a high-skill, fast-moving environment.

You will lead design reviews, mentor peers, and collaborate with Product, Engineering, and Research to translate complex user problems into shipped features.

Qualifications

• Production ML experience architecting and deploying ML systems at scale.
• Strong software design principles and robust systems focus.
• Understanding of modern foundation models, function calling, and edge-case handling in live environments.
• Ability to own projects end-to-end with high ownership and velocity.

Responsibilities

• Architect and ship production-grade ML systems powering agentic workflows.
• Own ML lifecycle including data curation, fine-tuning, inference optimization, evaluation, and monitoring.
• Build reliable interactions with external tools and APIs with safety and memory considerations.
• Translate research into high-availability software addressing real user workflows.
• Lead design reviews, set engineering standards, and mentor ML teammates.
• Collaborate with Product, Engineering, and Research to turn problems into shipped features.

Job description

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.

Technical 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.