AI Engineer jobs at AI companies

Retrieve the right context for a question and measure how often you do.

• Introducing Contextual Retrieval — Anthropic freeDone with Introducing Contextual Retrieval — Anthropic

  article — Hybrid search (embeddings plus BM25), reranking and chunk context, each with a measured drop in retrieval failures — design with numbers.

Checkpoint: Hybrid retrieval with a labelled query set — BM25 plus vectors fused with reciprocal rank fusion, a reranker, and 50 hand-labelled queries; report recall@k and nDCG against keyword search alone.

Expose a real system to agents through MCP, and measure whether they use it correctly.

• Model Context Protocol — introduction and spec freeDone with Model Context Protocol — introduction and spec

  docs — The protocol itself — tools, resources, transports — straight from the spec rather than from a framework's wrapper around it.

Checkpoint: An MCP server for an API you know, measured by an agent — Expose five real operations as tools, then run an agent on 30 tasks and count how often it picks the right tool with the right arguments; rewrite descriptions until that number moves.

Write a tool-using agent loop yourself and measure where it fails.

• Building effective agents — Anthropic freeDone with Building effective agents — Anthropic

  article — Workflows versus agents, and when you need neither: the handful of patterns these systems are built from, named by a lab that ships them.

• Hugging Face Agents Course freeDone with Hugging Face Agents Course

  course — Hands-on: tool calling, a ReAct-style loop and multi-agent setups in code you run — the gap between reading about agents and writing one.

Checkpoint: Write the agent loop yourself, then evaluate it — Messages API tool use with a step cap, tool-error retries and context trimming; score it on 30 real tasks for tool choice, argument correctness and steps taken.

Turn error analysis on production traces into evals that gate every change.

• AI Evals: Everything You Need to Know — Hamel Husain and Shreya Shankar freeDone with AI Evals: Everything You Need to Know — Hamel Husain and Shreya Shankar

  article — Start from error analysis on real traces, not from a metric; then judges you check against people — the practice behind 'evaluation frameworks' in these postings.

Checkpoint: An eval harness that gates a change — Label 100 real outputs by hand, build an LLM judge and measure its agreement with you, and make CI fail when a prompt or model change drops the score.

Fine-tune a small open model on one narrow task and prove it beats a prompted baseline.

• Hugging Face LLM Course freeDone with Hugging Face LLM Course

  course — Tokenizers, the Trainer, and fine-tuning chapters in runnable notebooks — enough to fine-tune a small open model on your own data.

• TRL documentation — Hugging Face freeDone with TRL documentation — Hugging Face

  docs — The library behind most supervised fine-tuning and preference tuning (DPO, GRPO) you will be asked about, with working recipes.

Checkpoint: Fine-tune against a prompted baseline — LoRA-tune a small open model on one narrow task and compare it with a prompted frontier model on the same eval set — quality, latency and cost per 1,000 calls.

Train a small language model and change one design choice with an ablation to show for it.

• Neural Networks: Zero to Hero — Andrej Karpathy freeDone with Neural Networks: Zero to Hero — Andrej Karpathy

  course — Backprop to a GPT in PyTorch, one video at a time — the fastest way for an engineer to read and debug model code.

• Learn the Basics — PyTorch tutorials freeDone with Learn the Basics — PyTorch tutorials

  docs — Tensors, autograd, datasets and the training loop in the framework's own words; a reference you will return to.

Checkpoint: Train a small GPT and change one thing — Reproduce a small model with nanochat or nanoGPT on a modest dataset, change one design choice, and show the ablation with its loss curves.

Post-train a small model with preference or verifiable rewards, and see where it games the reward.

• RLHF book — Nathan Lambert freeDone with RLHF book — Nathan Lambert

  book — Post-training as practised on language models — reward models, PPO, DPO, verifiable rewards — by a researcher who post-trains open models, free to read online.

• TRL documentation — Hugging Face freeDone with TRL documentation — Hugging Face

  docs — Runnable GRPO and DPO trainers, so the book's methods become an experiment on a laptop-sized model.

Checkpoint: Preference- or reward-tune a small model on a verifiable task — GRPO on arithmetic or unit-test-checked code with a small open model; plot reward against a held-out eval and explain where it starts to game the reward.

Serve an open model and measure latency and throughput under load.

• LLM Inference Handbook — Modular freeDone with LLM Inference Handbook — Modular

  docs — Latency, throughput and cost of serving a model — batching, KV cache, quantization, which engine when — as one practical handbook rather than twenty blog posts.

Checkpoint: Serve a small open model and measure it — Serve a small open model with vLLM, then measure time to first token and tokens per second at 1, 8 and 32 concurrent requests, with and without quantization.