07 - Picking a specialization¶

What this session is¶

"AI engineer" is not one job. It's six. This page lays them out, honestly, with skills, day-to-day, salary range, and how to break in.

Why specialization matters now¶

In 2020 you could be a generalist. In 2026 the field has differentiated. Hiring managers ask "what kind of AI engineer" and expect a specific answer. "All of it" reads as "none of it."

Pick one direction by month 4. Build your portfolio around it. You can always pivot - but pivoting without ever specializing means you never look hireable to anyone.

The six specializations¶

1. Applied LLM engineer¶

Day-to-day: Building product features on top of LLM APIs (OpenAI, Anthropic) or self-hosted models. Prompt engineering. RAG pipelines. Tool use. Evals. Agents.

Skills: Strong Python. API integration. Vector DBs. Frontend competence helpful. Eval discipline.

Salary range (US, 2026): $130-220K early career; $200-400K senior.

Hires the most. Lowest barrier to entry. Easy to demonstrate via portfolio (build apps). Most competitive for entry roles because everyone is targeting these. The senior end is wide open.

Break in by: Build 3 real LLM apps. Write them up. Contribute to LangChain / LlamaIndex / similar. Get good at evals.

2. Inference / serving engineer¶

Day-to-day: Making models run fast in production. vLLM, TGI, custom kernels. Latency optimization. Cost optimization. Quantization. Batching strategies. GPU scheduling.

Skills: Strong systems background (the "polyglot" engineer profile). C++/CUDA helpful. Kubernetes. Profiling tools. PyTorch internals.

Salary range (US, 2026): $180-280K early; $300-600K senior.

Hires steadily. Higher barrier; fewer candidates with the right systems background. Excellent fit if you came from backend/SRE.

Break in by: Contribute to vLLM. Benchmark inference setups. Write blog posts comparing serving strategies. Build a serving setup for an open model and document end-to-end.

3. Fine-tuning / training engineer¶

Day-to-day: Train custom models. Fine-tune with LoRA/full FT/RLHF/DPO. Manage training infrastructure. Hyperparameter sweeps. Data curation. Eval-driven iteration.

Skills: PyTorch deep. Distributed training (DeepSpeed, FSDP). Familiarity with paper-reading. Data work.

Salary range (US, 2026): $180-300K early; $300-500K senior.

Hires fewer than serving. Many companies use off-the-shelf models. Real demand at frontier labs (Anthropic, OpenAI, DeepMind, smaller research orgs).

Break in by: Fine-tune and publish 2-3 open models. Write up the experiment design. Contribute to trl, peft, transformers.

4. MLOps / platform engineer¶

Day-to-day: Build the platform other AI engineers use. Experiment tracking. Model registry. Deployment pipelines. Monitoring. Feature stores. Data pipelines for ML.

Skills: Strong infra/DevOps. Kubernetes. Airflow/Dagster. Familiar with the ML lifecycle. Some ML enough to talk to ML engineers.

Salary range (US, 2026): $160-260K early; $250-450K senior.

Hires consistently. The non-glamorous backbone. Often the easiest pivot from existing DevOps/SRE.

Break in by: Build an end-to-end MLOps stack for a personal project. Contribute to MLflow / Kubeflow / similar. Run your own model serving in K8s and document.

5. ML researcher / research engineer¶

Day-to-day: Reproduce papers. Run experiments. Propose new architectures, training objectives, evaluation methods. Write papers. (Research engineers do less paper-writing, more implementation.)

Skills: PhD or equivalent published work for researcher. For research engineer, very strong ML + systems but no PhD required. Mathematics. Paper-reading speed.

Salary range (US, 2026): $200-400K early; $400-1M+ senior at frontier labs.

Hires few but pays the most. Frontier labs. Highly competitive.

Break in by: This roadmap probably isn't your path. Research roles want PhDs or extraordinary equivalents. If you're set on this, the path is academia first.

6. AI safety / evaluation engineer¶

Day-to-day: Build evaluation pipelines. Red-team models. Measure capabilities and harms. Build alignment tooling. Write up findings.

Skills: Eval discipline. Critical thinking. Some ML. Strong writing.

Salary range (US, 2026): $180-300K early; $300-500K senior.

Hiring grew fast 2024-2025. Frontier labs and AI safety orgs (Anthropic, Apollo, METR, AI Safety Institutes). Mission-driven.

Break in by: Public evaluation work. Replicate published evals. Blog about failure modes. Apply to safety-specific fellowships.

How to choose¶

Three honest questions:

What's your existing background?
Backend/SRE → serving or MLOps.
Frontend/product → applied LLM.
Researcher/ML-adjacent → fine-tuning or research engineer.
Devops platform → MLOps.
What do you want your day to look like?
Building product? Applied LLM.
Debugging GPU memory? Serving.
Reading papers? Research engineer or fine-tuning.
Building pipelines? MLOps.
Probing failure modes? Safety.
What's the market in your area / target company?
Some specializations cluster geographically (research at SF/UK hubs; applied LLM everywhere).
Check job postings on companies you'd want to join. What's the actual mix?

What if you pick wrong¶

You can switch within the field. Applied LLM → serving is a common one. Serving → fine-tuning is harder (lots of math gap). Research → applied is easy (downward in pay, easier transition).

The cost of switching specialization mid-roadmap: 2-3 months of regroup. Not catastrophic. Don't paralyze yourself trying to "perfectly" pick.

What you might wonder¶

"Can I just be a generalist?" For your first AI role, no. The portfolio has to read as something specific. After 1-2 years on the job, generalist makes sense again.

"Aren't agents going to replace all this?" Maybe. Eventually. Not on a 12-month timeline. Build for the world that exists.

"Is it too late?" For research, harder than 5 years ago. For applied / serving / MLOps, the market is wider than ever because every company suddenly needs AI capabilities.

Done¶

Know the six specializations.
Have a leaning toward one (or two to compare further).
Will pick by end of month 4.

Next: Reading papers without drowning →