Founding AI Engineer at Noscen
Sunnyvale, CA
$150,000–$250,000
Aug 18, 2026
Sunnyvale, CA
Aug 20, 2026
What this job asks for AI summary
A founding-team AI engineering role at an early-stage startup, responsible for building the full intelligence layer of a personalized AI assistant from the ground up. The work spans multimodal context understanding, memory and personalization infrastructure, retrieval and reasoning systems, and the end-to-end pipelines, APIs, and privacy-first data handling that support them. Suited to a hands-on engineer comfortable owning ambiguous, greenfield AI product work.
Mid level · 3+ years · Bachelor's required · 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).
Estimated from BLS employment for this occupation and area, per-skill prevalence across our listing corpus, and published wage benchmarks — as of Aug 20, 2026. It is a model, not a headcount.
Why we read it this way (7)
No work location is stated anywhere in the posting — city, state, region, and remote status are all absent. The location fields have been left blank and remote is set to false as a conservative default.
The title 'Founding AI Engineer' carries no standard seniority level word. The requirements gate on 3+ years of overall experience and 2+ years of generative AI experience, which maps to Mid-level on the career ladder despite the founding-team framing.
The degree requirement is a hard gate: 'Bachelor's degree or above in CS or a related field' with no equivalent-experience escape clause.
DeepSeek, Qwen, and Llama are listed as examples of open-source models the candidate should be able to work with — they are treated as interchangeable illustrations of the same capability rather than separate hard requirements.
Docker, Kubernetes, and cloud platforms (AWS, GCP, Azure) appear under 'Bonus' and are preferred rather than required.
MCP servers appear in the required qualifications section but as one item in a list of 'or other ways of making models reason over context' — the requirement is on context engineering broadly; MCP servers specifically are captured as preferred to reflect the 'or' framing.
Compensation is described only as 'competitive base salary' with no figures given.
Read the full posting
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