AI/ML Engineer

The posting, as Backops Ai wrote it

About BackOps

BackOps is building the AI resolution layer for companies that make or move physical goods.

The physical economy still runs on a fragmented web of ERPs, WMSs, TMSs, carrier and vendor portals, email, spreadsheets, legacy systems, and the people who know how to hold all of it together. When something breaks, whether a shipment is delayed, an order changes, a claim needs to be filed, a document is missing, someone has to gather context across systems, determine what should happen next, take action, and make sure the problem is actually resolved.

BackOps automates that work.

Our platform, Relay, deploys AI operators that understand operational workflows, work across the systems our customers already use, take action, navigate exceptions, and carry work through to resolution. We aren't building another dashboard or copilot. We're building the execution layer that sits between systems and gets the work done.

We recently raised a $42M Series B led by Insight Partners, just six months after our Series A, and are scaling across some of the largest companies in logistics, manufacturing, retail, and other industries that power the physical world.

The opportunity ahead is much larger than any single workflow or vertical. We're building the infrastructure for AI to operate reliably inside the real world.

The Role

As an AI/ML Engineer, you'll own the infrastructure and systems that make Relay's underlying intelligence more capable, efficient, and reliable.

Building an AI operator that can reason through a workflow is one challenge. Making that intelligence perform consistently across thousands of real-world operational scenarios, fragmented enterprise systems, and unpredictable conditions is another.

That's the problem you'll help solve.

You'll work at the intersection of machine learning, distributed systems, and production AI, designing the infrastructure that supports model execution, inference optimization, evaluation, and continuous improvement. You'll help determine how we select, adapt, deploy, and optimize the models powering Relay, balancing intelligence and quality with the performance, scalability, and economics required for enterprise production.

This isn't a role focused on integrating AI APIs or maintaining an established ML stack. You'll make foundational technical decisions, build capabilities from the ground up, and work closely with our CTO and engineering teams to advance the intelligence behind our products.

This is a senior individual contributor role with extraordinary leverage. You'll help define the AI/ML foundation BackOps scales on.

What You'll Own

Build the infrastructure behind Relay's intelligence

Own the architecture and engineering of the AI/ML systems that power Relay, enabling our models to run reliably, efficiently, and at scale.

That may include

• Model serving, inference pipelines, and execution infrastructure
• Model selection, routing, and orchestration
• Shared interfaces for acces

[…]
architectures and engineering standards that other teams could build upon.

Experience with supply chain or logistics is useful. However, exceptional engineering judgment, AI/ML systems depth, and a track record of shipping production infrastructure matter more.

This Probably Isn't the Role for You If

• Your AI experience is primarily integrating third-party APIs or building lightweight LLM applications without meaningful infrastructure or systems ownership.
• Your background is concentrated in theoretical ML research or offline model development, and you haven't owned the engineering required to bring AI capabilities into production.
• You're most comfortable operating within an established infrastructure stack rather than defining foundational systems and architectural patterns.
• You think optimizing model performance is exclusively about improving benchmark scores, without considering reliability, latency, cost, and real-world outcomes.
• You prefer narrowly scoped implementation work over owning difficult technical problems from architecture through production.

Why BackOps

AI has become remarkably good at generating information.

The next frontier is much harder: AI that can be trusted to do the work.

In the physical economy, that means more than producing the right answer. An agent has to understand what's happening, make a decision, interact with real systems, navigate exceptions, communicate with real people, and remain accountable until the problem is actually resolved.

That creates an unusually challenging engineering problem.

You'll help build the AI/ML foundation that makes this possible, determining how models are deployed, evaluated, optimized, and continuously improved as Relay takes on increasingly sophisticated operational work.

Few engineering teams get the opportunity to shape the underlying intelligence and infrastructure of an emerging AI platform while the category itself is still being invented.

That's the opportunity at BackOps.