Machine Learning Engineer

About the Role

We're building the next generation of agentic AI systems, intelligent, autonomous agents that reason, act, and continuously improve. As a Machine Learning Engineer, you won't just build models, you'll architect the entire ecosystem where our AI agents live, learn, and operate.

This is a high-impact role for a product-minded, systems-level thinker who thrives in ambiguity and wants to shape foundational AI infrastructure from the ground up.

You'll work at the intersection of LLMs, distributed systems, and real-world applications, owning everything from core ML architecture to customer-facing experiences.

What You'll Do

• Architect & Build Agentic Systems
  • Design and develop our core agentic AI platform, enabling autonomous reasoning, decision-making, and continuous learning
  • Implement multi-agent orchestration frameworks (e.g., LangGraph)
• Own the ML & Data Infrastructure
  • Architect a modern lakehouse-based data platform
  • Build scalable data pipelines, feature stores, and real-time ML serving systems
• Develop LLM-Powered Applications
  • Build and optimize RAG systems, prompt pipelines, and reasoning workflows
  • Develop customer-facing applications, including a seamless AI chat interface
• Build Tool Machines for Agents
  • Create reliable, safe, and extensible tools that allow agents to interact with external systems, APIs, and data sources
• Drive MLOps & Model Lifecycle
  • Partner with data scientists to design infrastructure for training, fine-tuning, evaluation, and deployment
  • Implement robust experimentation, monitoring, and feedback loops
• Ship Production-Grade Systems
  • Write high-quality, scalable Python code
  • Ensure reliability, observability, and performance across distributed systems

What We're Looking For

Core Requirements

• 3–8 years of experience in Machine Learning Engineering or Software Engineering (ML-focused)
• Strong production experience with Python
• Hands-on experience with:
  • ML frameworks (e.g., PyTorch, TensorFlow)
  • LLMs, agentic frameworks (e.g., LangGraph), or RAG systems
• Experience designing scalable ML systems (training + serving)

Preferred Background

• Experience at top-tier tech companies (e.g., Meta, Google, Reddit, Pinterest)
• Combined experience across Big Tech + high-growth startup environments
• Background in ads, search, recommendation systems, or large-scale ML platforms
• Prior experience at a venture-backed startup

Nice to Have

• MLOps and infrastructure experience:
  • Kubernetes, MLflow, model serving systems
• Data engineering experience:
  • Spark, Airflow, dbt, ETL/streaming pipelines
• Experience designing systems using lakehouse architectures

Education

• Master's or PhD in Computer Science (or related field), OR
• Bachelor's degree + strong professional experience in software/ML engineering

Tech Stack

• Languages & Frameworks: Python, PyTorch, TensorFlow
• AI/LLM: LangGraph, RAG architectures
• Infrastructure: Kubernetes, MLflow
• Data: Spark, Airflow, dbt, lakehouse architecture

Who You Are

• Product-minded: You think about user experience, not just models
• Systems thinker: You design for scale, reliability, and extensibility
• Builder: You ship fast, iterate quickly, and thrive in ambiguity
• Impact-driven: You want to own and shape foundational technology

What Success Looks Like

• You've built scalable systems powering autonomous AI agents in production
• You've improved model performance and reliability through robust infrastructure and feedback loops
• You've delivered end-to-end ML products used by real customers

Why Join Us

• Build cutting-edge agentic AI systems from the ground up
• Own foundational architecture across the entire AI stack
• Work alongside a team operating at the intersection of LLMs, infrastructure, and product
• Massive opportunity for ownership, impact, and growth