Machine Learning Engineer / Technical Lead

Europe · Nigeria · Poland · Türkiye · Kenya · Latin America · Philippines · Indonesia · Ukraine · Hong KongFullTimePosted Jul 27, 2026

We’re hiring on behalf of A1, a high‑talent team building the next generation of AI‑native productivity applications. Their mission is to replace repetitive digital work with AI that can reliably complete real tasks for everyday users.

Rather than building another chatbot, A1 is creating long‑running AI workflows that manage conversations, coordinate actions, maintain context, and interact with external services — all with minimal user input.

Role

As a Machine Learning Engineer / Technical Lead, you will own critical ML subsystems in production. This is a hands‑on, high‑impact role focused on depth and reliability at scale.

What You’ll Do

  • Build end‑to‑end ML systems: data pipelines, training workflows, evaluation, inference, deployment.

  • Fine‑tune large models (LoRA, QLoRA, SFT, DPO, distillation).

  • Architect and operate scalable inference systems balancing latency, cost, and reliability.

  • Design and maintain data systems for synthetic and real‑world training data.

  • Implement evaluation pipelines for performance, robustness, safety, and bias.

  • Own production deployment: GPU optimization, memory efficiency, latency reduction.

  • Collaborate with backend, mobile, and desktop engineering teams to integrate ML systems.

  • Make pragmatic trade‑offs and ship improvements quickly, learning from real usage.

Expected Outcomes

  • Research models reliably translated into production with clear performance targets.

  • Stable, efficient, and maintainable ML pipelines and inference systems.

  • Fast detection and resolution of production issues.

  • Smooth collaboration across the team with minimal friction.

  • Iterations on models measurably improve user experience over time.

Tech Stack

  • Python

  • PyTorch / JAX

  • GPU‑based training & inference

Ideal Experience

  • You’ve built and shipped ML systems used by real users, not just demos.

  • Comfortable working with large models and understanding their failure modes.

  • Strong production‑grade coding skills with focus on correctness.

  • Self‑directed, pragmatic, and fully accountable for outcomes.

  • Clear communicator and effective collaborator in small, high‑trust teams.

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