Core Engineer - Software / Applied AI (Multiple Levels)

In your first 3 months, you will have:

Shipped at least one production AI capability (agents, evaluation, fine-tuning, or runtime optimization) that improves platform reliability, performance, or usability.

Established a strong evaluation and rollback model for at least one AI workflow operating within industrial production boundaries.

Earned trust through autonomy and execution—becoming a go-to owner for production AI platform capabilities.

In your first year, you will be:

Owning major components of the private AI platform end-to-end, with clear accountability for reliability, performance, and platform adoption.

Shipping repeatable AI structures that compress adoption cycles and scale across deployments (evaluation, guardrails, orchestration, optimization, operational playbooks).

Driving platform evolution through product enhancements grounded in real-world constraints and measurable outcomes, with safe rollout and rollback as a default.

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.

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).

At Edgescale AI, we’re deploying AI in the real world—helping customers apply this technology to unlock transformative productivity gains. Our work sits at the intersection of infrastructure, security, networking, and AI, where reliability and performance are non-negotiable and where solutions demand deep, distributed systems thinking.

We’re intensely AI-native. We build with AI, we ship AI, and we use it every day to accelerate how we design, test, deploy, and operate complex systems. If you want to help pave the application of AI in the real world, at global scale, we want to hear from you.

Edgescale AI is building an inclusive, merit-based organization. We are an equal opportunity employer and do not discriminate on any legally protected status. We value diversity, inclusion, and a shared passion for creating real-world impact.