New opportunity

AI Engineer

Distyl AI · San Francisco; New York, United States

About this role

Distyl AI seeks an AI Engineer to build and operate production AI systems in customer environments. The role involves translating ambiguous operational needs into reliable system behavior, working directly with customers and subject matter experts, and taking responsibility for whether deployed systems deliver value under enterprise constraints.

Responsibilities

Design, implement, deploy, and improve end-to-end AI systems. Build compound workflows that combine models, prompts, agents, tools, retrieval, evaluation, feedback loops, and execution.
Develop maintainable Python services and application logic that integrate AI capabilities with customer workflows, data platforms, APIs, and existing applications.
Measure live-system behavior, identify failure modes, debug issues, and iterate to improve quality, reliability, and usefulness. Build evaluation frameworks, test cases, feedback mechanisms, and observability patterns.
Clarify requirements and tradeoffs with customer stakeholders and subject matter experts. Collaborate with other engineers and AI Strategists on designs that balance speed, robustness, maintainability, and customer impact. Use AI-native tools for implementation, experimentation, debugging, and analysis.

Requirements

5+ years of software engineering experience and experience building applications powered by LLMs or other AI models, including integrating components into end-to-end systems.
Strong Python and production engineering fundamentals, including versioning, debugging, testing, performance, code review, and production readiness. The role requires ownership of production outcomes, clear communication with customer teams, comfort with ambiguity, and a pragmatic delivery approach.
Willingness to travel typically 10–30%, depending on the project and customer needs.

The role is based in San Francisco or New York and follows a hybrid schedule with 3+ in-office days per week (Tuesday–Thursday). Base salary is $180K–$250K, depending on experience, location, and level, with equity and benefits. Benefits include medical, dental, and vision coverage for employees and dependents, flexible time off, retirement and financial planning resources, wellness and family-building benefits, and in-office lunches and snacks.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

PythonLarge language modelsPrompt engineeringAI agentsRetrievalAI evaluationObservabilityAPI integrationSoftware testingDebuggingData analysisCode reviewCustomer collaborationAI workflows

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Own production outcomes
The role is accountable for whether deployed AI systems behave reliably and deliver value in customer environments, not just whether a demo works. Prepare to explain how you measured outcomes and changed a live system when results fell short.
Build coherent AI workflows
The posting calls for end-to-end systems combining models, prompts, agents, tools, retrieval, evaluation, feedback, and execution. Prepare to explain why particular components were needed and how their behavior fit together.
Engineer maintainable integrations
AI capabilities must connect through Python services and application logic to customer workflows, data platforms, APIs, and existing applications. Prepare to discuss production-readiness decisions, including testing, debugging, performance, and code review.
Evaluate and improve live behavior
The job explicitly includes measuring systems, diagnosing failures, and building test cases, feedback mechanisms, and observability. Prepare an example of how evidence from use led to a specific improvement.
Turn customer needs into system behavior
Engineers work directly with customer stakeholders and subject matter experts, and collaborate with AI Engineers and AI Strategists on tradeoffs. Prepare to show how you clarified an ambiguous workflow and communicated limitations and design choices.

Likely challenges to think through

Defining success amid ambiguity
A likely difficulty is converting messy operational needs into testable behavior without waiting for perfect requirements. Prepare a way to identify users, intended outcomes, constraints, and assumptions with stakeholders before choosing an implementation.
Diagnosing failures across components
In a compound workflow, a poor result may come from retrieval, a model, a tool, an integration, or their interaction. Prepare to describe how you would trace execution, isolate a failure, and use targeted tests and feedback rather than changing prompts blindly.
Balancing delivery speed with production robustness
Customer needs may evolve while deployed systems still need to be reliable and maintainable. Prepare to explain how you would choose a pragmatic initial scope, make tradeoffs explicit, and use evaluation and production observations to guide iteration.

Practice your answers

Technical practice

How would you design an end-to-end AI workflow for an operational task with unclear requirements?

What to coverPreparation suggestion: Explain how you would turn user and subject-matter-expert input into observable system behavior, then choose where models, prompts, retrieval, tools, and execution belong. Discuss how you would validate the workflow against its intended production outcome rather than a demo.

When would you add retrieval to a model-powered workflow, and how would you assess whether it helps?

What to coverPreparation suggestion: Discuss the information the workflow needs, how retrieved material reaches the model, and what can go wrong when it is missing or irrelevant. Compare results on representative tasks before and after the change.

How would you decide whether an AI workflow needs an agent or a simpler sequence of tool calls?

What to coverPreparation suggestion: Explain the trade-off between flexibility and predictable execution. Describe how you would test tool selection, execution outcomes, and failure handling in either design.

How would you structure a maintainable Python service that connects an AI workflow to customer applications, APIs, and data platforms?

What to coverPreparation suggestion: Separate integration logic from AI workflow logic and explain how you would handle changing interfaces and errors. Address testing and code review as part of keeping the service maintainable.

How would you build an evaluation framework for an AI system used in a customer workflow?

What to coverPreparation suggestion: Define test cases with users and subject matter experts, and distinguish component-level behavior from end-to-end usefulness. Explain how evaluation results would guide changes to prompts, retrieval, or workflow logic.

A live AI system becomes less useful after deployment. How would you diagnose the cause?

What to coverPreparation suggestion: Start with measured behavior and observability, then investigate failures across inputs, model behavior, retrieval, integrations, and execution. Explain how you would test a suspected cause before making a production change.

How would you turn user feedback into reliable improvements to a deployed AI system?

What to coverPreparation suggestion: Explain how you would collect and categorize feedback, connect it to observed behavior, and convert recurring issues into test cases. Describe how you would check that an iteration improves user value without introducing new failures.

What would you review before declaring a Python-based AI system ready for production?

What to coverPreparation suggestion: Cover versioning, tests, debugging support, performance, and code review alongside end-to-end system behavior. Explain which shortcomings would block deployment and which could be monitored and improved after release.

How would you balance delivery speed, robustness, and maintainability when a customer needs an AI workflow quickly?

What to coverPreparation suggestion: Identify the behavior that must work reliably for users and the trade-offs you would make explicit to stakeholders. Explain how evaluation and production observations would inform the next iteration.

How would you use AI-native engineering tools without losing confidence in the code or analysis they produce?

What to coverPreparation suggestion: Discuss their use in implementation, debugging, experimentation, or data analysis, as described in the role. Explain how you would independently test outputs and review changes before relying on them in production.

Behavioural practice

Tell me about a time you turned an ambiguous operational need into a working system.

What to coverPreparation suggestion: Use situation, action, and result to show the questions you asked, how you clarified desired behavior, and what changed for users. Distinguish your decisions from assumptions you still needed to validate.

Describe a time you took ownership of a production system that was not delivering its intended value.

What to coverPreparation suggestion: Give the situation, your diagnostic and corrective actions, and the observed result. Explain how you communicated progress and learned from the system's behavior.

Tell me about a design decision you made with engineers and non-engineering collaborators under competing priorities.

What to coverPreparation suggestion: Set out the situation, the speed, robustness, maintainability, or customer-impact trade-off, and your role in reaching a decision. State the result and what evidence showed whether the choice worked.

Describe a difficult conversation with a customer or subject matter expert about a system limitation.

What to coverPreparation suggestion: Explain the situation, how you made the limitation and available options clear, and what action you agreed on. Include the result for the workflow or working relationship without claiming the limitation disappeared.

Tell me about a time you adopted an AI tool to improve your engineering work.

What to coverPreparation suggestion: Describe the situation, how you used the tool for implementation, debugging, experimentation, or analysis, and how you checked its output. Give the result, including any limits you found.

Sharpen your knowledge

Topics to prepare

Compound AI system design
The posting requires end-to-end systems that combine models, prompts, agents, tools, retrieval, evaluation, and execution. Exercise: sketch a workflow for an operational request, marking each component’s purpose, handoffs, and likely failure modes.
Production reliability and ownership
Distyl emphasizes useful, reliable behavior in customer environments rather than demos. Exercise: define what successful live behavior would look like for a hypothetical workflow, then reason through how you would detect and respond to a degradation.
Evaluation and feedback design
The role calls for test cases, evaluation frameworks, feedback mechanisms, and observability. Exercise: design a small evaluation plan with representative cases, failure categories, a measure of quality, and a way to incorporate user feedback.
Customer requirements and engineering tradeoffs
The posting calls for clarifying ambiguous needs with stakeholders and SMEs while balancing speed, robustness, maintainability, and impact. Exercise: turn a vague operational request into questions, proposed system behaviors, and explicit tradeoffs.

Skills to revise

Maintainable Python integration
The posting requires Python services that connect AI capabilities to customer workflows, data platforms, APIs, and existing applications. Exercise: write a small Python service around a model-driven step and an API interaction; add tests for failed or malformed responses.
Workflow implementation
The role involves composing AI components into working systems. Exercise: implement a minimal prompt-and-retrieval workflow with a tool call, then trace how inputs, retrieved material, and tool results affect its output.
Live-behavior debugging
The posting explicitly calls for measuring behavior, finding failure modes, and iterating on deployed systems. Exercise: inspect sample request traces, classify failures by component, make one targeted change, and rerun the same test cases.
Production engineering review
Versioning, testing, performance, code review, and production readiness are named fundamentals. Exercise: review a small Python change for correctness, maintainability, test coverage, and performance risks; write a concise release-readiness checklist.
AI-assisted engineering
The posting requires daily use of AI tools for coding, debugging, design exploration, and analysis, without naming a specific product. Exercise: use an AI tool to propose a code fix, then verify it with tests and explain what you accepted or rejected.

Your preparation checklist

Prepare an end-to-end AI system walkthrough
The role calls for production AI systems, not demos. If you have a relevant example, make a one-page walkthrough showing the user need, architecture, integrations, deployment, your decisions, and observed production outcomes.
Practice a compound-workflow design exercise
Sketch how you would turn an ambiguous customer workflow into system behavior. Show where models, retrieval, tools, execution, and feedback belong; explain what you would leave out and why.
Gather evidence of Python production engineering
Prepare specific examples of maintainable Python services and API or data-platform integrations. Be ready to explain testing, versioning, code review, debugging, performance, and production-readiness decisions.
Build an evaluation and incident narrative
The posting emphasizes live-system measurement and iteration. Prepare a sample evaluation plan with test cases, feedback signals, and observability, plus a real example, if available, of a failure you diagnosed and improved.
Rehearse a customer requirements conversation
Draft questions for a stakeholder and subject matter expert about workflow, users, constraints, and success criteria. Practice explaining limitations and a speed-versus-robustness tradeoff in plain language.
Show your AI-native working method
Prepare a brief example of how you use AI tools to implement, debug, experiment, or analyze data, including how you check their output before relying on it.
Write targeted questions for Distyl AI
Bring questions about how engineers and AI Strategists divide design decisions, how production value is measured with customers, and how travel and the Tuesday–Thursday in-office schedule work in practice.

Availability last checked: Nov 4, 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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