Job Description

We're building the operating system for the next generation of computing — one
where AI agents replace apps and your technology finally works for you instead
of the other way around.
We're a stealth-mode startup with a world-class founding team with deep roots
in consumer AI, extended reality, and wearable technology — including founders
of some of the most recognizable hardware and software platforms of the last
decade. We're backed by strategic partnerships with leading silicon and
manufacturing companies, and we're hiring our first AI engineer to build the
intelligence layer at the core of the platform.
This is a rare opportunity to architect the agent infrastructure of a platform
that doesn't exist yet — at the layer where always-on contextual AI meets a
wearable form factor for the first time.
Additional product details shared under NDA.

THE ROLE
The backend engineers build the infrastructure. The mobile engineers build the
user facing surfaces. You build what runs between them — the agents
themselves.
As our first AI Engineer you will own the design and implementation of our
agent layer — the pipelines, reasoning chains, memory retrieval systems, tool
integrations, and orchestration logic that turn raw LLM capability into a
platform that genuinely replaces the app paradigm. You will work directly with
the CEO and across the full engineering team to make sure the agent experience
is as technically rigorous as it is experientially compelling.
This is a hands-on engineering role. You will write production code, own the
agentic runtime architecture, and be directly accountable for the quality of
every agent interaction on the platform. You will also be a key voice in
decisions about which models to use, how to route between them, and how to
structure the memory and context systems that make our platform smarter over
time.
We actively use AI development tools across our engineering team — Cursor,
Claude, Copilot — and expect engineers who use them seriously as a core part
of their workflow.

WHAT YOU'LL BUILD
The platform agent runtime — the core orchestration layer that manages agent
sessions, chains reasoning steps, routes to tools, and executes actions on
behalf of users
Multi-provider LLM integration and routing — selecting and switching between
regional and task-specific language models dynamically, with latency, cost,
and capability all factored into routing decisions
RAG architecture and memory retrieval — the systems that give agents access to
the user's persistent, encrypted context layer and make responses smarter and
more relevant over time
Tool and skill integration pipelines — the infrastructure that connects agents
to external APIs, device capabilities, and first-party platform features
Agent evaluation and observability — the frameworks that measure agent
quality, surface failures, and give the team visibility into how agents are
actually performing in production
On-device inference optimization — working with the mobile and firmware teams
to identify which parts of the agent pipeline can run locally on device,
reducing latency and cloud dependency as the platform evolves toward wearable
hardware
Prompt architecture and system design — the structured prompting frameworks,
system instructions, and context management patterns that govern agent
behavior consistently across the platform

WHAT WE'RE LOOKING FOR
4+ years of software engineering experience with at least 2 years working
directly on LLM-based systems in production
Deep hands-on experience with LLM integration — not just API calls but genuine
understanding of how to build reliable, scalable, low-latency AI pipelines
Strong experience with RAG architectures — vector databases, embedding
pipelines, retrieval optimization, context window management
Familiarity with agent frameworks and orchestration patterns — LangChain,
LlamaIndex, AutoGen, or similar, with a clear point of view on their strengths
and limitations
Solid Python engineering skills — you are writing production code, not
research notebooks
Experience evaluating LLM outputs at scale — building evals, measuring
quality, detecting regressions
Genuine understanding of the tradeoffs between different LLMs — capability,
latency, cost, privacy implications — and experience making routing decisions
in production systems
Active user of AI-assisted development tools with a genuine point of view on
how to use them well
Strong written English and proven ability to work effectively in a remote and
distributed team
Must be authorized to work in the US
Strong plus: experience with on-device or edge inference — Core ML, ONNX,
TensorFlow Lite, or similar; multi-agent system design; always-on or streaming
inference architectures; privacy-preserving AI systems; wearable or mobile AI
platform experience

WHAT WE OFFER
Salary: competitive depending on experience
Meaningful early-stage equity
Full medical, dental, and vision coverage
Fully remote with occasional in-person time in Silicon Valley or Paris for key
milestones
Awear is an equal opportunity employer. We celebrate diversity and are
committed to creating an inclusive environment for all employees.