Machine Learning Engineer
This is the employer's own posting, not a copy on a job board.
What we know
Is it still open?
Confirmed still open
Last checked 1d ago — checked against the employer's own applicant tracking system, which is the company answering directly.
We re-read the employer's own applicant tracking system and the posting was still there. That is the company answering directly.
How old is it?
Posted 49d ago
The date the source published, not the day we noticed it (2026-07-27). Last seen at its source 3h ago.
Is it remote?
Marked remote on the employer's board
Their board carries a remote setting on this posting — a field they filled in, not wording we read. The location field names somewhere specific, which is usually where the team or the entity sits.
Who may apply?
Europe, Singapore, Malaysia, Nigeria, LATAM, Philippines, Indonesia, Ukraine, Hong Kong, South Africa
The description states no restriction of its own. This is the source's own tag.
Pay not stated
Similar roles pay €75k–114.5k/yr
Middle 50% of 80 listings that do state pay — Engineering · all levels · Europe · EUR/year. This employer has published no salary; this is what comparable listings we hold disclose, never converted between currencies or periods. How this is calculated.
Skills named in the ad
Recognised terms only, from a fixed vocabulary — this is what CV matching compares against.
Carried by 1 source
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ashby employer's own board first seen 11d ago · last seen 3h ago
The listing
We’re hiring on behalf of ActAI, 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.
As a Machine Learning Engineer, 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 core ML systems powering a proactive, long‑horizon AI product.
Own the full lifecycle: data preparation, training, evaluation, inference, iteration.
Turn research ideas into production systems that run reliably.
Debug model failures and system issues using real production signals.
Ship quickly, measure outcomes, refine, and repeat.
Collaborate closely with research, product, and engineering teams.
Mentor and review work from other ML engineers.
Work under real production constraints: latency, cost, reliability, safety.
Tech stack
Python
PyTorch / JAX
GPU‑based training and inference systems
Ideal background
Experience building and shipping ML systems used by real users.
Strong understanding of how modern ML models behave — and misbehave — in production.
Ability to write production‑quality code and think in systems, not scripts.
Independent ownership: driving work across the finish line.
Fast learner, clear communicator, iterative mindset.
Expected outcomes
ML models and systems consistently meet accuracy, latency, reliability, and efficiency targets.
Complex production issues are monitored, debugged, and resolved with minimal disruption.
Training, inference, and data pipelines are robust, scalable, and maintainable.
Measurable improvements in ML systems based on real‑world signals and user feedback.
Technical guidance and mentorship that raises the overall ML engineering standard.
Seamless integration of ML features into products that meet business goals.
How ActAI works
Our client A1 is a small, world‑class team with high talent density. They move quickly, make decisions collectively, and balance shipping high‑quality work with rapid learning. Structure, sound judgment, and the ability to execute independently are highly valued.
Interview process
3–4 interviews with technical team members.
Conducted virtually and/or onsite.
Transparent and efficient decision process.
Successful candidates will receive an offer to join a team building AI that delivers practical benefits to billions of users globally.