Senior Machine Learning Engineer

We are looking for a Senior Machine Learning Engineer to take ownership of critical ML systems powering an advanced, proactive AI product.

This is a hands-on role for an experienced engineer who is comfortable taking ambiguous technical challenges, designing practical solutions and bringing them all the way into reliable production.

You'll work across the full ML lifecycle — from data preparation and training to evaluation, inference and continuous improvement — with a strong focus on building systems that perform reliably at scale.

What you'll do

• Build core ML systems supporting a proactive, long-horizon AI product
• Own ML projects end-to-end, covering data preparation, training, evaluation, inference and iteration
• Turn research concepts into practical systems that can operate reliably in production
• Investigate model failures and broader system issues using real production data and signals
• Follow a continuous improvement cycle: ship, measure, learn and iterate
• Partner closely with research, product and engineering teams to translate ML capabilities into tangible user value
• Mentor other ML engineers and contribute to raising the team's technical standards through hands-on guidance and code reviews
• Design and operate systems under real-world constraints such as latency, cost, reliability and safety

Tech stack

• Python
• PyTorch / JAX
• GPU-based training and inference systems

You'll join a small, high-talent-density and hands-on team working on an ambitious AI product. The team moves quickly while maintaining a strong focus on quality and learning. Decisions are made collaboratively, but engineers are expected to bring structure to ambiguous problems, exercise technical judgment and take significant ownership of their work.

This role is particularly suited to someone who enjoys going deep into technically challenging problems and seeing their work make a direct impact on a production product.

What success looks like

In this role, you'll be responsible for making ML systems more capable, reliable and effective in production.

Success means:

• Production ML models consistently meet their accuracy, latency, reliability and efficiency targets
• Complex production problems are effectively monitored, investigated and resolved with minimal disruption
• Training, inference and data pipelines remain robust, scalable and maintainable
• ML systems show measurable improvement based on real-world signals and user feedback
• Other ML engineers benefit from your technical guidance and mentorship
• ML capabilities integrate smoothly with the broader product and contribute to meaningful business outcomes

• Has built and shipped machine learning systems used by real customers
• Understands both the capabilities and failure modes of modern ML models in production environments
• Writes high-quality production code and approaches problems from a systems perspective, rather than treating them as isolated scripts
• Takes ownership and can independently drive complex work through to completion
• Learns quickly, communicates clearly and uses iteration to continuously improve their work