Applied ML Engineer

You’ll sit at the intersection of machine learning and backend engineering, owning how our systems work in production. This is a senior production AI systems role focused on making intelligent systems fast, reliable, and scalable.

Your key responsibilities

• Build and evolve production ML-powered backend systems: services, pipelines, and online components that drive product behavior.
  
  

• Own end-to-end “intelligence flow” through the product.
  
  

• Implement across the stack

  • ML code: Python (Rust is welcomed)

  • Non-ML services/infrastructure: GO

  • Cloud/infra: mostly AWS, some GCP
    
    

• Work on hard applied problems: long-tail retrieval, relevance tuning, evaluation design, and system-level quality improvements.
  
  

• Operate systems in production: monitoring, capacity planning, and continuous improvement.
  
  

• Proactively partner in product decisions and planning

This role is ideal for someone who

• Enjoys thinking about vector spaces and distributed systems in the same day
  
  

• Cares about both ML quality and production reliability
  
  

• Wants to build products and infrastructure, not just experiments
  
  

What Makes This Role Different

• You won’t just “deploy models” — you’ll shape how intelligence flows through the entire product
  
  

• You’ll work on real-world AI problems: long-tail retrieval, relevance tuning, system latency and user experience
  
  

• You’ll have technical ownership over the core architecture of the company

• Startup mindset required - be able to “hustle” your way to solution - learn as product priorities progress i.e. write some React or Typescript should the need arise

Required qualifications

• Senior level (5+ years) backend engineering skills
  
  

• Hands-on experience with AWS (GCP is a plus), deployment and scaling, containerization, IaC with focus on best practices and efficiency
  
  

• Solid understanding of embeddings, representation learning, tree and graph-based models
  
  

• Familiarity with metric learning concepts and experience working with vector search or ANN indexing systems
  
  

• Collaborative mindset: seeking feedback early, participating in design reviews, and helping to maintain shared standards and docs
  
  

• Comfortable explaining technical concepts to different audiences (engineers, product, leadership), including making assumptions explicit
  
  

Preferred qualifications

• Experience with designing and consuming APIs
  
  

• Experience with semantic search or recommender systems and embedding-based retrieval pipelines
  
  

• Ability to tune and evaluate metric learning approaches