AI Engineer in ML Data at Logical Intelligence
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Jul 25, 2026
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Jul 26, 2026
What this job asks for AI summary
An AI engineering role centered on building and maintaining ML and data pipelines for distributed model training and validation. The work spans researching reasoning algorithms, developing benchmarking tools, building synthetic data generation infrastructure, and tuning frameworks to accelerate ML development. Suited to someone with a graduate background in a relevant field and hands-on production experience in ML infrastructure and distributed computing.
Mid level · 3+ years · Master's required
“or” means any one of them counts — you don't need all of them.
Posted 2 times — it's one opening, so apply once.
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
Most people in this occupation already list these. Still required — just not what gets you shortlisted.
What the occupation pays Median $122,874 (middle half $87,544–$162,374).
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 (7)
Location is not stated anywhere in the posting; CBSA and state fields are left blank. The company website (logicalintelligence.com) gives no city, so the role may be fully remote or based outside the US — defaulting to the US-based flag of true per instructions.
The role sits at the boundary between ML Infrastructure/DataOps engineering (15-1252) and Data Science/ML research (15-2051). The primary day-to-day work — designing ML/data pipelines, distributed training infrastructure, and synthetic data generation — leans toward ML engineering, but the explicit research component (new reasoning algorithms, benchmarking, deep learning) tips it toward 15-2051. Alt code 15-1252 is a genuine runner-up.
Degree requirement: the JD states 'You have an M.Sc.' as a hard qualification gate, so Masters is set as the minimum required degree.
PyTorch is listed first and most prominently; TensorFlow and JAX are listed as slash-alternatives in the same requirement and are captured in 'alternatives'.
Azure, AWS, and GCP are listed together as cloud platform options; Azure is used as the primary name with the others as alternatives.
Multi-node/multi-GPU training and Formal Verification appear under the 'Bonus Points' section and are marked as preferred accordingly.
No compensation range is provided.
Read the full posting
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