Supernal helps small-to-medium businesses hire their first AI employee. Our AI teammates are built using intelligent, agentic workflows deployed on a proprietary platform. We deliver working, value-generating AI Employees—not tools—that handle real business processes alongside human teams.
The Role
We’re hiring a
Senior AI Engineer
to build and ship the first generation of
personalized, self-improving agentic workflows
that users rely on daily. This is an “end-to-end” role: you’ll design the agent runtime, memory + retrieval systems, evaluation harnesses, and the product-facing surfaces that put agents in front of real users at scale.
You should be equally comfortable reasoning about distributed systems and data (latency, caching, queues, failure modes, cost) as you are with modern agent stacks (tool use, memory, RAG, multi-step planning, guardrails, and evaluation).
This role will partner closely with platform engineering to leverage and extend our core services (Django backend, event-driven systems, Kubernetes, observability) while owning critical parts of the AI application layer.
What You’ll Build
Personalized agent runtime:
Agentic workflows that adapt to a user’s preferences, data, and ongoing behavior over time.
Memory & retrieval systems:
Short/long-term memory, durable state, and retrieval pipelines across vector DBs and relational data.
Voice experiences (real-time + async):
Speech-to-speech/voice agents, streaming audio pipelines, turn-taking, interruption handling, latency tuning, and QA for natural conversations.
Agent evaluation + reliability:
Offline/online evals, regression suites, red-teaming, monitoring, and rollout controls so agents are trustworthy in production.
Production agent infrastructure:
Scalable orchestration patterns for multi-step jobs, background tasks, and user-facing interactions (sync + async), with clear SLAs/SLOs.
Tooling + developer experience:
Libraries and primitives that make it easy for the team to build new agent capabilities quickly and safely.
What You’ll Own (Responsibilities)
Ship user-facing agent experiences end-to-end: prototype → production → iteration based on real usage.
Architect and implement
stateful agent systems
(workflows, tool calling, memory, retrieval, and human-in-the-loop where needed).
Build voice features end-to-end where they unlock value: realtime speech agents, voice UI/UX, prompt/audio routing, and guardrails for safe tool execution.
Build/own an
evaluation harness :
curated test sets + scenario suites
automated scoring / rubric-based graders
prompt/model/version tracking
canary + A/B experimentation and safe rollout patterns