Core Engineer - Software / Applied AI (Multiple Levels)

Edgescale AI•Denver, CO

About The Position

We’re looking for a Core Engineer focused on Software / Applied AI to build the production AI capabilities that make our edge platform scalable, reliable, and repeatable. You’ll own core pieces of our private AI platform—agentic and multi-agent systems, repeatable AI structures, evaluation and reliability mechanisms, and platform capabilities like auto fine-tuning and runtime optimization of infrastructure and models. This work operates within clear industrial production boundaries: AI can suggest and act only within well-defined limits, and we do not ship AI behavior into industrial production without evaluation, clear ownership, and a way to roll back. You’ll partner with data and infrastructure teams for requirements and feedback, then generalize learnings into platform capabilities that scale across deployments. This is a hands-on role for someone who thrives in a high-ownership setting and wants to build the infrastructure that makes real-world AI possible.

Requirements

6+ years building and operating production software systems; experience shipping AI-enabled platforms or agentic systems is strongly preferred.
Strong fundamentals in distributed systems, performance, and reliability; comfort owning production services end-to-end (e.g., Docker/Kubernetes deployments, APIs via REST/gRPC, and strong production discipline around rollout and rollback).
Experience building evaluation frameworks, monitoring, and safety/guardrail systems that enable controlled AI behavior in production (e.g., automated eval harnesses, drift/quality monitoring, tracing, and structured telemetry).
Strong engineering craft: clean implementations, thoughtful designs, operational clarity, and strong documentation (e.g., Python and/or TypeScript/Go, FastAPI-style services, and effective testing practices).
Comfort working in ambiguity and making sound trade-offs under real constraints (latency, cost, GPU utilization, and reliability).
Clear communicator and strong collaborator across engineering and commercial teams.
Ownership mindset: outcomes over tasks.

Nice To Haves

Production experience building agentic and multi-agent systems, orchestration layers, and evaluation frameworks with clear reliability goals (e.g., tool calling, workflow orchestration, and evaluation loops that are measurable and repeatable).
Experience designing repeatable AI structures (tool calling, memory/state patterns, policy constraints, safety/guardrails) that can be reused across applications and deployed through stable APIs.
Building fine-tuning workflows and runtime optimization systems for private AI deployments, including performance and cost trade-offs (e.g., inference optimization, batching/caching, GPU efficiency, and vLLM-style serving).
Experience building monitoring and quality systems for AI behavior that enable measurable improvement over time and safe rollback (e.g., offline/online evaluation, tracing, structured logs, metrics, and incident-driven iteration).
Strong systems instincts across data, infrastructure, and security constraints that impact AI in production (e.g., event/data systems like Kafka, operational stores like Postgres/time-series databases, and secure deployment patterns).

Responsibilities

Build and operate production AI capabilities including agentic and multi-agent workflows, tool calling, orchestration, and repeatable patterns that scale.
Design and implement evaluation, monitoring, and quality systems that make AI behavior measurable, reliable, continuously improving, and safe in production.
Build platform capabilities for private AI, including auto fine-tuning workflows, model/runtime optimization, and performance improvements for inference under real constraints.
Implement safety and operational controls so AI behavior is bounded and production-ready, including policy constraints, approval workflows, auditability, and rollback mechanisms.
Develop pragmatic interfaces and APIs that make AI capabilities easy to integrate across platform services and customer environments.
Improve developer velocity through automation and tooling, using AI tools to accelerate implementation, tests, documentation, and iteration loops, then refining with engineering judgment.
Partner with data and infrastructure teams to ensure the right context reaches inference and agent workflows with predictable latency, reliability, and cost.
For senior roles: mentor engineers, review designs, and raise the technical bar across the organization.