Description review

SentiLink | Engineering & Data Science | REMOTE, HYBRID, and ONSITE (USA) | Full Time

SentiLink · REMOTE, HYBRID, and ONSITE (USA) · back to the listing

HR standards

38/100

poor

Title ↔ description

62/100

needs work

Reads as

Unclear

no confident match

What this role officially is

data scientist — ESCO, the EU occupation classification

Data scientists find and interpret rich data sources, manage large amounts of data, merge data sources, ensure consistency of data-sets, and create visualisations to aid in understanding data. They build mathematical models using data, present and communicate data insights and findings to specialists and scientists in their team and if required, to a non-expert audience, and recommend ways to apply the data.

Also known as: data scientists, data engineer, research data scientist, data expert, data research scientist

How others title the same work

Large employers

  • Applied AI Engineer Automattic
  • Machine Learning Engineer, CX Intelligence Coinbase
  • AI Engineer - FDE (Forward Deployed Engineer) Databricks
  • AI Engineer - FDE (Forward Deployed Engineer) - U.S. Federal Sector Databricks
  • Senior AI Engineer – Notebooks Datadog

Startups

  • Attendi | Medior Machine Learning Engineer | Amsterdam, Netherlands | ONSITE (hybrid) | €6,000 - €7,000 per month | Full-time (80–100%, ~4–5 days/week) | Visa sponsorship + 30% ruling possible Attendi
  • Member of Technical Staff (applied) Anthrogen
  • Aptura AI | Full-Time | MTS (Applied AI), MTS (SWE / Product) | London | ONSITE / HYBRID Aptura AI
  • Arcforma AI (arcforma.ai) | AI Engineer (Marketing / Construction / Arcforma AI (arcforma.ai)
  • Staff AI Engineer - Agent Architecture & Behavior Artisan

What the listing never says

  • No section describes what the person would actually do. Scope clarity
  • No pay range published. Candidates cannot tell whether applying is worth their time. Pay transparency

The listing, marked up

Nothing in the wording of this listing tripped a check. The scores above still judge how complete and coherent it is.

Primarily hiring in the U.S. for more senior level roles.
We’re hiring across Engineering (Full-stack and Infra) and Data Science / ML roles. Hiring at Manager/Director level as well as mid-level/Senior levels. We’re language-agnostic and hire for fundamentals.
Across the team you’ll see: • Python, Go, Rust, Scala • PostgreSQL, Snowflake • Kafka, Redis • Terraform, Kubernetes • ML: XGBoost, PyTorch, Feature stores, real-time scoring pipelines
If you love solving complex distributed systems challenges, building customer-facing ML products, or working on detection systems that must be both high-precision and low-latency, you’ll fit right in.
Roles are here: https://jobs.ashbyhq.com/sentilink?utm_source=hacker_news
If you have questions, feel free to reach out directly at liz.woodfield at sentilink dot com.
At SentiLink, we stop identity fraud at scale. Our products protect banks, fintechs, marketplaces, and leading financial institutions from synthetic identity fraud, identity theft, and emerging threat. We analyze millions of applications while keeping real users moving fast.
We’ve seen tremendous traction and are growing extremely quickly. Our real-time APIs have helped verify hundreds of millions of identities, starting with financial services and rapidly expanding into new markets. SentiLink is backed by world-class investors including Craft Ventures, Andreessen Horowitz, NYCA, and Max Levchin.
SentiLink supports a variety of ways to work, ranging from fully remote to in-office. We operate as a digital-first company with strong collaboration across the U.S. and India. We maintain physical offices in Austin, San Francisco, New York City, Seattle, Los Angeles, and Chicago in the U.S., and in Gurugram (Delhi) and Bengaluru in India. If you’re located near one of these offices, we would love for you to spend time in the office regularly. Some roles are hybrid or in-office by design. For example, our engineering team in India works primarily from our Gurugram office.
We’re a small, high-impact team solving one of the most interesting and adversarial problems in fintech: how do you build systems that reliably determine whether a real human is on the other side of a financial transaction?

How this was produced

Highlights are found by rule, not by a model: each one is a phrase matched at a known position, and every note is a template we wrote. The two scores come from a typed-decision model (Jev) that reads the listing against the official role definition and real listings for the same role, and returns probabilities rather than prose — it never writes any of the words on this page, and never chooses what to highlight.

Deterministic penalty applied to the HR score: 12 points (from 50 before penalties). Reviewed 21 Sep 2026.