Software Engineer II - AI

McGraw Hill LLC.

1d•Remote

About The Position

At McGraw Hill, our AI Platform team is building intelligent learning experiences used by millions of students and educators worldwide. We're not bolting AI onto legacy products — we're rethinking how people learn by putting generative AI, retrieval-augmented generation, and agentic workflows at the center of the experience. This is applied AI with real stakes. The models are powerful, the problems are genuinely hard, and the impact — helping a student finally grasp a concept they've been struggling with — is something you'll actually feel. We're looking for a Software Engineer II — AI to join our AI Platform team. You'll build the services and APIs that use AI to power student and teacher experiences — RAG pipelines, LLM orchestration, retrieval and routing layers, the production infrastructure that turns model capabilities into shipped features. A note on what this role is and isn't: This is a software engineering role, not a data science or ML role. You won't be training models, fine-tuning, building eval harnesses for model performance, or running ML experiments — we have an elite team of data scientists and applied researchers who own that work. Your job is to take what they produce and the model APIs we use and turn them into reliable, observable, scalable production systems. If your background is primarily in MLOps, model training, or data science and you're hoping to do more of that here, this isn't the right fit — but we'd encourage you to check our other openings. This is a role designed for a software engineer who's ready to go deeper. You've got a couple of years of professional experience, you've shipped real things, and you're excited about working on AI-powered products at a level beyond tutorials and side projects. Senior engineers will be around to pair with, learn from, and review your work — but increasingly, you'll be the one breaking down problems, proposing approaches, and owning features through to production. This is a remote position open to applicants authorized to work for any employer within the United States.

Requirements

Roughly 2-5 years of professional software development experience.
Shipped features that real people used, debugged production issues, and learned that "works on my machine" isn't a finished state.
Comfort in any modern backend language — Python preferred, but if your background is heavier on Go, Java, Kotlin, or Node and you're confident you can ramp in Python, tell us about that.
Experience building APIs and working with relational databases (PostgreSQL, MySQL, or similar) is the baseline.
Curiosity about AI, with some skin in the game.
Engaged with AI as a software engineer — built something that calls an LLM, broken something, formed opinions about prompt design or retrieval quality, played with frameworks like LangChain or LlamaIndex.
Good engineering instincts.
Know when to ask for help and when to push through.
Write tests because you've been burned by not writing tests.
Read more code than you write, and can explain why you made the choices you did.
Comfort with ambiguity at your level.
Increasingly be the one breaking down medium-sized features, identifying edge cases, and flagging risks early.
Past the stage of needing tasks handed to you in step-by-step form.
Clear communication. You can explain a tradeoff in writing without 14 paragraphs.
Give code review feedback that's specific and kind.
Ask questions in public channels because you've figured out that other people probably have the same question.
A "low-ego, high-ownership" disposition.
See a problem and want to fix it.
More interested in the team shipping the right thing than in being the one who shipped it.
Willing to be wrong in public so you can learn faster.

Nice To Haves

Production experience with AWS or Azure
Familiarity with frontend work (React or Angular) — you don't have to live there, but you can help when needed
Experience with vector databases, embeddings, or RAG architectures
Exposure to Go, or genuine willingness to learn it
Familiarity with Kubernetes, Terraform, or modern CI/CD (GitHub Actions)
Background in edtech, accessibility (WCAG 2.2 AA), or content-rich product domains
Open-source contributions, technical writing, or teaching of any kind

Responsibilities

Build the services that deliver AI to users. Pick up work on our RAG pipelines, LLM orchestration layers, and the APIs that surface AI capabilities to users.
Integrate with model providers (Azure OpenAI and others), wire up retrieval and routing logic, and build the production glue that turns research into shipped product.
Start with well-scoped features and grow into owning them end-to-end as you ramp.
Work across our backend stack. Most of your time will be in Python (FastAPI, async/await, Pydantic), with opportunities to contribute to Go services as you grow.
Touch PostgreSQL, async task workers, and the integrations that connect our services together.
Write code that's tested, readable, and considerate of the people who'll maintain it after you.
Participate in code reviews — both giving and receiving — and learn how your team thinks about quality, observability, and reliability.
Get good at AI engineering — the software side. You don't need to arrive as an expert. You do need to be the kind of person who reads the docs, runs the experiments, asks the awkward questions, and forms a real point of view about how to build with LLMs reliably — prompt design, retrieval quality, latency and cost tradeoffs, graceful failure modes. We'll invest in your growth here.
Collaborate broadly. You'll work with data scientists evaluating model outputs, product managers shaping features, designers thinking about UX, and other engineers across the org.
Communicate clearly — in PRs, in design discussions, in Slack — is part of the job.
Grow into more. Over time, you'll start influencing design decisions on your team, mentoring engineers newer than you, and taking on larger pieces of work.