Forward Deployed Engineer
Posted 379 days ago, which is unusual. The employer's own board was still carrying it when we last read it, 1 hour ago.
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 2d 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 379d ago
The date the source published, not the day we noticed it (2025-09-26). Last seen at its source 1h ago.
We have tracked this listing since 5 Oct 2026 (5 days). The employer's own board has carried it every time we have read it, most recently 1 hour 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?
Japan
The description states no restriction of its own. This is the source's own tag.
Pay not stated
Similar roles pay $130k–182.5k/yr
Middle 50% of 45 listings that do state pay — Engineering · all levels · APAC · USD/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 5d ago · last seen 1h ago
The listing
About Chalk
Chalk is building the data platform that powers the future of machine learning applications. We tear down complexity, latency, and scale barriers that have traditionally constrained ML capabilities. Our platform combines Rust-speed performance with elegant tools that developers love to use. Leading companies depend on Chalk for everything from stopping fraudulent credit card swipes, verifying identities, and maximizing clean energy capture. We've recently raised a $50 million Series A, led by Felicis.
About the role
You are an exceptional software engineer who is able to build bespoke technical solutions and partner with machine learning teams to develop Chalk’s proprietary infrastructure. You will work with customers of Chalk to build efficient feature pipelines for problems in healthcare, finance, and recommendation systems. This role will give you a unique opportunity to work closely with customers, gathering requirements and engineering feature pipelines that support use-cases like cancer detection, fraud prevention, and product recommendation. This is your opportunity to join us in-person as an early employee and make a significant impact at a high growth start-up.
What you will do
Write code to implement Chalk technology for Chalk customers and prospects. You will become familiar with Chalk infrastructure and find the best way to integrate and implement them in different environments
Work closely with our Engineering and Sales teams
Act as the primary technical point of contact pre-sales and post-sales (when we are onboarding a new customer or supporting an existing one)
Recommend new products to customers as their business evolves
Help interview and grow the Engineering team
What we’re looking for
Technical background including experience writing software or building ML models
4+ years experience of professional backend software engineering experience
Proficient in Python, SQL
Ability to collaborate effectively in teams of technical and non-technical individuals
Excellent written and verbal communication, problem-solving, storytelling, and analytical skills
Experience working closely with sales teams and customers is a plus.
Bachelor's degree in Computer Science or equivalent
Bonus points
Previous experience working with ML/data products or services
Understanding of Machine Learning Ops/Infrastructure
Previous experience as a forward-deployed engineer in a high-growth startup focusing on Machine Learning