AI Platform Developer
Qube Research & Technologies · New York, NY
$180,000–$300,000from the description
Jul 9, 2026
New York, NY
Jul 21, 2026
What this job asks for AI summary
An early-career engineering role focused on building and maintaining an internal AI platform at a quantitative investment firm. Day-to-day work spans developing RAG pipelines, agentic workflows, retrieval infrastructure, and internal APIs, as well as ensuring reliability through monitoring, evaluation frameworks, and incident response. Best suited to a recent graduate or junior engineer with a grounding in machine learning and Python who wants to bring LLM-based systems into production.
Junior level · National · Full-time
“or” means any one of them counts — you don't need all of them.
We read this from the posting text with AI. Skim the description below before ruling yourself out.
How this req sits in the market our data
Rare in this occupation — lead with these, and say what you built with them.
Most people in this occupation already list these. Still required — just not what gets you shortlisted.
What the occupation pays Median $138,970 (middle half $107,524–$175,762). This posting is about at that midpoint.
Estimated from BLS employment for this occupation and area, per-skill prevalence across our listing corpus, and published wage benchmarks — as of Jul 28, 2026. It is a model, not a headcount.
Why we read it this way (8)
The posting explicitly targets early-career engineers and recent graduates, with no stated minimum years of experience — seniority is assessed as Junior despite the high compensation range.
The degree requirement lists Bachelor's, Master's, or PhD but also accepts 'equivalent practical experience', so no formal degree is hard-gated.
No work location or city is specified in the posting; QRT is a global firm with offices in multiple countries. The compensation is quoted in USD, suggesting a US-based or US-remote position, but the location is genuinely unclear.
The role sits at the boundary between Software Developer (building production AI services and APIs) and Data Scientist (ML/LLM foundations, transformers, RL); primary day-to-day work leans toward building and operating production software, so 15-1252 is preferred with 15-2051 as the runner-up.
PyTorch is listed as the named deep learning framework but the JD explicitly accepts 'another deep learning framework' — common alternatives (TensorFlow, JAX, Keras) are captured in alternatives.
Cloud platform exposure (e.g. AWS) appears in the required section but is framed with 'e.g.' and 'exposure to', indicating familiarity rather than a hard gate; however, per section-placement rules it is retained as a required skill with alternatives for the other major clouds.
The 'Nice to Have' section items (RAG, vector databases, agentic AI, ML deployment, observability tooling, open-source/Kaggle contributions) are all marked preferred.
Reinforcement learning is mentioned as part of the AI/ML foundation requirement but names no specific framework or tool, so it is not emitted as a standalone skill.
Read the full posting
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