Description review

AI Engineer

Direct Supply, Inc. · United States · back to the listing

HR standards

55/100

needs work

Title ↔ description

77/100

solid

Reads as

Machine Learning Engineer

85% confident

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

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What the listing never says

  • 25 bullet points. Long requirement lists deter qualified candidates, who read them as hard gates. Scope clarity
  • No pay range published. Candidates cannot tell whether applying is worth their time. Pay transparency
  • No location or timezone policy stated, so a candidate cannot tell where they may work from. Scope clarity

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.

About the Role

Direct Supply isbuildingAI systems that change how care is delivered to millions of seniors and support the people who care for them.We'rehiring engineers to design, ship, andoperatethose systems in production.

This role spans the full product lifecycle: discovery,experimentation,application delivery, and deployment.You'llwork directly with customers and product teams, owntechnicalcalls, and see yourproduct used at scale.

We expect technical rigor,architecturediscipline,strong product judgment, anda track recordof shipping. In return, you getautonomyand real customer impact.

WhatYou’llDo

• Design, build, andoperatesystems in customer-facing production environments

• Translate ambiguous business and customer problems intoprototypes & technical specs

• Track AIcapabilitiesand apply them where they create clear leverage

• Ownthe fullapplicationlifecycle:productdiscovery, experimentation, evaluation, deployment, and monitoring

• Ownsystem design, tradeoffs,andlong-termscaling andmaintainability

• Buildcodebasesthatarelegible to agents and other developers;drive the organization forward ontooling.

• Partner withproduct managerstoenhance theirproduct vision, includingwithAI-native solutions

WhatWe’reLooking For

• Strong applied AI and software engineering fundamentals

• Builderswho canspantech,product,and designthinkingwithhigh autonomy

• Bias for shipping, iterating, and following customer feedback over polish

• High ownership& agency— measured by outcomes, not deliverables

• Curiosity to improve systems, products, and your own craft

What We Work In

Youdon'tneed every one of these, but this is the stackyou'llbe productive in:

• Python, C#,TypeScript,PostgreSQL;

• Frontier model ecosystems & agent frameworks (e.g., Anthropic, OpenAI)

• Docker, Terraform, and AWS

Minimum Qualifications

• 2+ years insoftware engineeringor applied AI

• ExperienceworkingwithAIdevelopmenttoolsinfull-stack applications

• ExperiencedesigningAI-basedsolutionstorealworkflows

• Working knowledge of cloudand frontier AIplatforms

Nice to Have

• Degree in Computer Science, Engineering, Data Science, or a related field

• Experience operating AI systems in production, with attention to evaluation, cost, and performance tradeoffs

• Background in high-ambiguity environments with proximity to customers (e.g., early-stage startups, forward-deployed engineering, internal product teams)

• Experience in large-scale Python, C#, and TypeScript codebases

• Experience integrating AI solutions into existing,establishedproducts.

• Experience working with healthcare, regulated, or sensitive data

Job to be performed in the location listed. Generous benefit package available.

© 2013 to 2026 Direct Supply, Inc. All rights reserved.

Originally posted on Himalayas

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 67 before penalties). Reviewed 21 Sep 2026.