Forward Deployed Engineer jobs at AI companies

Retrieve from the customer's own documents and measure how often the right passage comes back.

• 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 the customer's systems to the agent through MCP, with tool descriptions tuned until it picks them 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.

Build a tool-using agent for someone else's workflow, with step limits and error handling you can explain.

• 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.

Build an eval set from the customer's real cases and report quality in their terms before and after each 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 report a customer can read — Fifty real cases from one workflow, labelled with the person who does the work today; a before-and-after table for one change, and the three failure types that remain.

Fine-tune a small model for one narrow customer task and compare it with a prompted frontier model on quality, latency and cost.

• 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.