Member of Technical Staff, Machine Learning

Our client is looking for a Member of Technical Staff, Machine Learning to help build core machine learning components for a proactive AI assistant.
The successful candidate will work on real production systems from day one, gaining hands-on experience with how large-scale machine learning behaves outside research environments.
This role is suited to an engineer who wants to develop strong systems judgment by shipping, debugging, evaluating, and improving real-world ML systems. They will work closely with experienced machine learning engineers and product teams while gradually taking greater ownership of technical initiatives.

Responsibilities

• Build and improve machine learning components across data preparation, training, evaluation, and inference

• Fine-tune and adapt models as part of larger production systems

• Implement evaluation and testing frameworks to better understand model behaviour and performance

• Help build and maintain pipelines for real-world and synthetic training data

• Investigate model failures, performance issues, and production incidents

• Ship improvements iteratively and use real user feedback to guide further development

• Work closely with senior machine learning engineers, product teams, and other technical stakeholders

• Contribute to systems operating under production constraints, including latency, cost, reliability, scalability, and safety

• Improve the quality and maintainability of ML systems through testing, monitoring, and continuous iteration
  

Requirements

• Strong foundations in machine learning and modern neural network architectures

• Hands-on experience training, fine-tuning, evaluating, or deploying machine learning models

• Strong Python programming skills; experience with PyTorch and/or JAX

• Familiarity with production machine learning systems running on GPUs

• Ability to write clean, reliable, and production-quality code

• Understanding of the machine learning lifecycle, including data preparation, training, evaluation, inference, and deployment

• Ability to learn new tools and technologies quickly

• Ability to work through ambiguous technical problems with guidance

• Willingness to take increasing ownership as experience and confidence grow
  

WHAT THEY OFFER

• Cash and equity compensation

• Remote-first setup with flexible hours as part of a distributed, global team

• Generous paid time off

• Company laptop provided

• A quick hiring process – 3, occasionally 4, interviews, with fast decisions afterwards
  

ABOUT THE COMPANY
They’re an early-stage AI company building a proactive assistant aimed at the 5+ billion people currently stuck using non-AI-native tools for everyday things – email, notes, tasks.The focus is squarely on reliability: long-running workflows, persistent context, and tasks that actually get done, even though the underlying models aren’t fully deterministic.
Stage: Early-stage AI startup
Focus: Proactive AI assistant for everyday productivity
Work mode: Remote-first, distributed team