Software engineer, human feedback systems

A software engineer specialising in human feedback systems designs and operates software for collecting, annotating and transmitting human judgements for AI models. They build the interfaces used by evaluators, the services that organise data exchanges and the pipelines that make this feedback usable for training or evaluation. Their work focuses on the software system that connects these activities.

This scope brings together web interface development, backend services and data flows. The engineer may follow a feature from the evaluator’s screen through to its use by a technical team. They work with researchers, engineers and programme managers to clarify collection needs and turn them into usable features. Reporting lines and the division of decision-making responsibilities remain for each organisation to define.

The French title “Ingénieur logiciel des systèmes de retour humain pour l’IA” describes the software engineering specialisation presented here. Job advertisements from OpenAI and Anthropic corroborate its core responsibilities in two US AI laboratories. They do not establish a standalone occupational title or recruitment demand in France. Nor do they justify assigning the entire learning chain or general scientific autonomy to the role.

Human feedback systems engineer, evaluator and AI researcher: who does what?

• The engineer develops and maintains the tools used to present tasks, collect responses and transmit structured data.
• The evaluator or human expert uses these tools to make judgements. Building their interface does not mean carrying out all the annotation.
• The AI researcher expresses training or evaluation needs and works with the engineer to translate them into infrastructure. Developing the platform does not automatically transfer scientific decisions to the engineer.

The first challenge is to make human feedback usable beyond the interface where it is entered. Collection may work for evaluators while remaining difficult for the teams training or evaluating models to use. Recruitment therefore needs to cover both the interfaces and the services that transmit structured responses, with explicit responsibility for their operation in production.

Annotation quality deserves attention separately from software availability. HumanSignal points out that poor annotations used without review can degrade the performance of a model trained on them. This does not mean an engineer can guarantee the quality of judgements. Their system must enable the relevant people responsible to examine the data and understand what has been collected. The choice of evaluation criteria and decisions about instructions must have named owners.

Fictional example: a team collects several assessments of the same model response. If it keeps only the final result, the people responsible for review can no longer examine individual opinions. When defining the software requirements, the team could ask for human responses and model suggestions to be stored separately. Argilla provides a documented example of this distinction, without setting a mandatory design for every project.

Another challenge is adapting tools as research needs evolve. Candidates need to be able to clarify what is worth building and then explain their choices to technical colleagues. Recruiting solely on the basis of a programming language would overlook this ability to define the work.

Finally, specify the level of autonomy you need. Taking responsibility for an interface and its pipeline does not imply deciding the architecture of the entire platform alone. The architectural responsibilities described in a Staff+ role represent a particular scope, which should be distinguished from the expectations set for each hire.

• Design the collection and annotation interfaces used by evaluators, experts and researchers.
• Develop the backend services and APIs that connect human tasks to AI systems.
• Build pipelines that transmit structured feedback to training or evaluation activities.
• Improve evaluators’ tools in collaboration with the teams that use the collected data.
• Turn the collection needs of researchers and programme managers into clearly defined software work.
• Maintain robust human feedback systems and take responsibility for operating them in production.

The capabilities to look for are primarily those of software engineering: designing coherent interfaces and services, developing pipelines and taking responsibility for a system used in production. A background in full-stack development can be a relevant starting point if the candidate’s work demonstrates these responsibilities. Their qualification title tells you less about this specialisation than the software they have actually built and maintained.

To assess these capabilities, look for work that makes the links between the screen, service and data clear. Candidates should be able to specify their personal contribution, the users they served and how the data reached its destination. Experience building annotation or evaluation tools is particularly close to this role. Other software experience should still be considered according to the responsibilities actually held, rather than solely on whether AI appears in someone’s background.

Understanding the training and evaluation cycle of large language models helps in discussions with researchers. Anthropic cites this familiarity in its job advertisement. It helps candidates understand where feedback goes and what needs to be collected, without establishing a requirement to create a model alone or conduct alignment research.

For initial qualifications, the same advertisement accepts a bachelor’s degree or an equivalent combination of education, training and relevant experience. This US employer’s condition does not establish a mandatory French qualification. For your hire, state the capabilities expected and the level of support available. In particular, distinguish delivering a defined feature from scoping a project and making platform architecture choices.

To assess this profile, use the criteria and practical exercises below, adapting them to the software and responsibilities of the role.

1. Define a framework tied to the expected work

Distinguish four criteria: full-stack design, human feedback integration, production operation and clarification of needs. For each, specify the expected outcome and the level of autonomy required.

Link the first criterion to an interface and its backend service. Link the second to the transmission of structured responses to their intended use. Reserve broader architectural decisions for roles that actually include them.

Treat an explanation that connects each choice to a user and a destination for the data as a positive signal. Pay attention to answers that indiscriminately assign annotation, development and scientific decisions to the role.

2. Examine a system used in production

Ask candidates to present a system they have built or maintained. Have them specify their personal contribution, the interfaces involved and the services for which they were responsible.

Trace a piece of data with them from entry through to use. Ask what operational difficulties they encountered and how they developed the software in response.

Value their ability to explain the limits of their work. Probe answers that remain focused on tool names without describing what the tools enabled users to do.

3. Set a case close to the role

Fictional example: you want to collect several human opinions on model responses, then transmit the data to an evaluation team.

Ask candidates to sketch the interface, services and exchanges required. Have them distinguish human responses from suggestions generated by a model. Invite them to specify the information retained and the limits of the proposed export.

Then introduce differing opinions. Ask how they would make these available for examination by those responsible for review, without asking them to decide alone which is the correct answer.

To examine their approach to verification, ask how they would test the path from entry to export. Assess how well their checks match the expected functions, without imposing a universal protocol.

4. Examine collaboration and trade-offs

Present a change in requirements during collection. Ask what clarification candidates would seek before changing the tool and which decisions they would have confirmed by the researchers or programme managers.

Look for an ability to discuss a feature’s usefulness and explain the consequences of building it. If the role includes technical coordination, ask them to specify how they would divide responsibilities between contributors.

Do not equate this coordination with line management. Assess the latter separately if you actually intend to include it in the role.

5. Cross-check observations and references

Involve an engineer able to examine the software design and someone who understands the intended use of the human feedback. If this expertise is unavailable internally, have an external technical specialist examine the case.

With the candidate’s consent, seek references on their actual responsibilities and collaboration with users or researchers. Compare this information with the work presented and the limits they themselves have described.

Decide on the basis of these cross-checked observations. A fluent presentation or experience at a well-known laboratory does not replace examining personal work and the level of autonomy required.