The Opportunity

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.

What You’ll Do

• 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.

What Success Looks Like

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.

Who You Are

• 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.

Unique Experiences We Value

• 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).

Benefits

• We work in a high-ownership, real-world startup environment where you’ll move fast, build new systems, and see your impact immediately—what you ship runs in the field and drives measurable customer outcomes.

• We work alongside AI every day. Writing static code, docs, or plans “by hand” is no longer accepted—here you’ll use the latest AI tools to iterate and ship faster and to apply AI with our customers at scale.

• You’ll take on elite technical challenges at the frontier of infrastructure, including next-generation cloud and IoT, hardware/software/networking in real-world edge environments, the foundation for data and AI inference, and industry-leading secure systems in demanding operational (OT) settings.

• You’ll learn fast by working with exceptional teammates and collaborating directly with industry leaders as partners in software, AI, and infrastructure.

• Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. This role may be filled at the Senior or Staff level, with a base salary range of $130,000–$155,000 (Senior) or $160,000–$185,000 (Staff).

• Total compensation for this role includes equity in your work. You are eligible for meaningful equity through stock options in an early-stage, high-growth company.

• You are eligible to participate in company benefit plans, which may include health, dental, and vision coverage, a 401(k) with company match, flexible PTO, paid parental leave, commuter benefits, and relocation and visa support for eligible roles.

Edgescale AI

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.