Machine learning engineer career path

The staff-level calls are about how every model at the company gets trained, versioned, served and rolled back. Build one of those pieces well and you're already doing the narrow version.

Usually when

Seven or more years in, once other teams depend on infrastructure you designed

Skills to add

• Distributed training with PyTorch FSDP or Ray
• GPU capacity planning and cost control
• ML platform design (feature store, registry, serving)
• Writing technical strategy documents

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ML infrastructure several teams use, and what it did to training time, serving cost or deploy frequency.

LLM apps fail in ways you already know how to handle, like weak evaluation, drift, slow responses and serving cost. Retrieval, fine-tuning methods and prompt behavior are the new material.

Usually when

Any time after 2-3 years of production ML

Skills to add

• Fine-tuning with LoRA or QLoRA
• Inference serving with vLLM
• Retrieval-augmented generation and vector search
• LLM evaluation and red-teaming

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An LLM system you shipped or prototyped, with measured quality, latency and cost.

You use the model CI, registries, retraining jobs and monitoring every day, so you know exactly where they hurt. MLOps makes fixing them your job.

Usually when

2-4 years in, if the pipeline and deployment work interests you more than the models

Skills to add

• Kubeflow Pipelines or Vertex AI Pipelines
• Kubernetes and Helm
• Model registry and promotion workflows in MLflow
• Drift monitoring with Evidently or WhyLabs

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Pipeline or deployment automation you built, and how much it cut the time from trained model to production.

Research engineers turn papers into working training runs. If you already read papers and reproduce results for your own models, you're closer than you think.

Usually when

Three or more years in, usually with published work or serious open-source contributions behind you

Skills to add

• Reproducing papers from scratch
• PyTorch custom modules and training loops
• Experiment design and ablation studies
• Writing for workshop or conference submission

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A reproduced paper, open-source contribution or published result, with the code public.

AI products need PMs who know what a model can do reliably, what testing it costs and when a simple rule beats a model. You already answer those questions in planning meetings.

Usually when

4-6 years in, if you've been shaping which model problems are worth solving

Skills to add

• Writing PRDs with model quality targets
• Customer discovery interviews
• Designing human review and fallback flows
• Product analytics and experiment readouts

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A model-driven feature where you influenced scope or launch criteria, with the user or business result.