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Applied ML Engineer

  • Department: Engineering

  • Reports to: CTO

  • Work location: On-site/Hybrid

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.

Essential duties and 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

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