New opportunity

AI Researcher

Octave-X · Chicago, United States

About this role

Octave-X seeks an AI Researcher to develop methods for safe, verifiable and interpretable AI, with particular relevance to the mathematical guarantees of its Tenzin product. The role combines independent theoretical research with experiments and collaboration to turn findings into production-grade enterprise systems.

Responsibilities

Develop approaches to verifiable, controllable and interpretable AI behavior using formal methods and type-theoretic foundations.
Design and run reproducible experiments assessing safety, alignment, robustness and model quality.
Publish peer-reviewed findings at venues such as NeurIPS, ICML, ICLR, CAV and POPL, and present results to internal and external audiences.
Work with ML engineering and infrastructure teams to prototype, validate and productionize research for Tenzin and the Octave-X platform.
Shape the long-term safe-AI research roadmap, identify promising research problems and mentor junior researchers.

Required qualifications

A PhD or equivalent research track record in machine learning, formal methods, programming languages or a closely related field; a strong record of top-tier ML or programming-language/verification publications, or demonstrated applied research impact in industry; mathematical fluency in proof-based reasoning, type theory, category theory or related frameworks; and proficiency prototyping in Python with modern ML stacks such as PyTorch and JAX. Candidates must be able to balance theoretical rigor with product constraints and communicate complex ideas clearly to researchers and engineers.

Preferred qualifications

Research in type theory, theorem proving, program synthesis or Homotopy Type Theory; experience with AI alignment, red-teaming, adversarial robustness or constitutional AI; experience shipping research prototypes to customers; and familiarity with Lean, Coq, Agda, Isabelle or formally verified software development.

This is a full-time, hybrid position in Chicago, IL, with compensation listed as $120,000 plus benefits.

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

Skills for this role

Formal verificationAI safetyAI alignmentModel interpretabilityFormal methodsType theoryCategory theoryMachine learningPythonPyTorchJAXExperimental designResearch publicationTheorem provingProgram synthesisHomotopy Type TheoryRed-teamingAdversarial robustnessConstitutional AILeanCoqAgdaIsabelleScientific communicationMentoring

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

Formal foundations for trustworthy AI
Develop methods for verifiable, controllable and interpretable AI behavior using formal methods and type-theoretic foundations, with particular relevance to Tenzin’s mathematical guarantees.
Rigorous empirical validation
Design reproducible experiments that assess safety, alignment, robustness and model quality.
Research-to-production translation
Prototype and validate ideas with ML engineering and infrastructure teams, then help bring research outputs into Tenzin and the Octave-X platform.
Publish and communicate findings
Produce peer-reviewed research and explain results clearly to both research and engineering audiences.
Set direction and mentor
Identify high-leverage problems, contribute to the long-term safe-AI research roadmap and mentor junior researchers.

Likely challenges to think through

Defining what a guarantee covers
Preparation hypothesis: formal claims about an AI system may be narrower than the behavior observed in deployment. Prepare to explain a method’s assumptions, what is proved, what remains empirical and where a guarantee stops.
Balancing rigor with product constraints
Preparation hypothesis: a theoretically strong approach may be difficult to prototype or deploy within engineering constraints. Prepare an example of choosing a tractable scope while preserving meaningful validation and stating the trade-offs.
Reconciling multiple evaluation goals
Preparation hypothesis: gains in safety or interpretability may come with costs to robustness or model quality. Prepare to discuss reproducible baselines, metrics and how conflicting experimental results would inform the next research decision.

Practice your answers

Technical practice

How would you define a verifiable safety claim for an AI system?

What to coverSuggested preparation: State the property, assumptions, system boundary, and evidence needed to support the claim. Explain what remains unverified and how you would avoid treating a limited proof as a general safety guarantee.

How could type-theoretic foundations help specify controllable AI behavior?

What to coverSuggested preparation: Describe a possible formal specification and what a proof or type judgment would establish. Distinguish the mathematical model from the behavior of an implemented, deployed system.

When would a proof assistant be useful in this research?

What to coverSuggested preparation: Discuss how a tool such as Lean, Coq, Agda, or Isabelle could help check a formal claim, without assuming the role uses a particular one. Address the effort required to formalize assumptions and maintain proofs as a system changes.

How would you evaluate whether a method makes model behavior more interpretable and controllable?

What to coverSuggested preparation: Define observable criteria for both properties and compare the method against a baseline. Consider whether an explanation reflects the behavior being controlled, rather than merely appearing plausible.

How would you design an experiment to assess an alignment or safety method?

What to coverSuggested preparation: Specify the hypothesis, comparison, relevant benchmarks, and measures of both safety and model quality. Explain how you would interpret gains on a benchmark without assuming they establish alignment in every setting.

How would you investigate failures under adversarial or out-of-distribution conditions?

What to coverSuggested preparation: Propose a systematic way to generate challenging cases and categorize failures, drawing on the posting’s interest in robustness and red-teaming. Discuss trade-offs between improved robustness, model quality, and the scope of the tests.

What would make your experimental results reproducible?

What to coverSuggested preparation: Cover documented configurations, data and benchmark choices, evaluation procedures, and variability across runs. Explain how you would separate a robust finding from sensitivity to a particular setup.

How would you structure a Python ML prototype for rapid research and reliable validation?

What to coverSuggested preparation: Discuss clear interfaces between the research idea, model implementation, and evaluation, using PyTorch or JAX as relevant examples rather than presumed requirements for a specific project. Explain what you would test before relying on the prototype’s results.

How would you decide whether a research prototype is ready for production work?

What to coverSuggested preparation: Identify the evidence and unresolved risks you would bring to ML engineering and infrastructure collaborators. Discuss how deployment constraints could change the design without silently weakening a formal claim.

What would make a safe-AI research direction both publishable and useful for Tenzin?

What to coverSuggested preparation: Frame a novel question, its proposed contribution, and evidence that could substantiate it. Explain how you would assess its relevance to formal verification and Tenzin’s stated mathematical guarantees without assuming a particular product implementation.

Behavioural practice

Tell me about a time you chose a research direction with substantial uncertainty.

What to coverSuggested preparation: Use a real situation, the evidence and trade-offs behind your choice, and the actions you took to test it. Give the result, including what changed in your assessment if the initial idea did not hold up.

Tell me about a time you worked with engineers to move research toward a usable system.

What to coverSuggested preparation: Describe the situation, your distinct contribution, and how you handled a conflict between research rigor and implementation constraints. State the result without overstating what reached production.

Tell me about a time you explained a complex technical result to different audiences.

What to coverSuggested preparation: Give the situation, how you adapted the explanation for researchers and engineers, and what each audience needed to decide. Describe the result and how you checked for understanding.

Tell me about a time you mentored a junior researcher through a difficult finding.

What to coverSuggested preparation: Describe the situation, your coaching actions, and how you upheld scientific rigor or intellectual honesty. Explain the result for the research and for the person’s development.

Tell me about a time a result challenged a planned publication or research roadmap.

What to coverSuggested preparation: Set out the original expectation, the conflicting evidence, and how you responded or communicated the finding. Give the result and what it changed about the work’s next steps.

Sharpen your knowledge

Topics to prepare

Formal guarantees for AI behavior
Revision exercise: Define a narrow AI behavior property, state its assumptions, and distinguish what a proof could establish from what experiments could only support. The role focuses on verifiable, controllable behavior and Tenzin’s mathematical guarantees.
Proof-based reasoning and type-theoretic foundations
Revision exercise: Work through a small proof using types or another formal framework, explaining each assumption and inference. Proof-based reasoning and familiarity with type theory, category theory, or related frameworks are required; Homotopy Type Theory is preferred.
Alignment and interpretability
Revision exercise: Formulate a testable question about whether a model’s behavior follows an intended constraint, then explain what the observations would and would not reveal about alignment or interpretability. Both are central to the advertised research focus.
Safety and robustness evaluation
Revision exercise: Sketch an evaluation plan covering safety, alignment, robustness, and model quality, including comparison conditions and limitations. The posting explicitly calls for rigorous experiments across these areas.
Research questions under deployment constraints
Revision exercise: Compare two proposed safe-AI research directions by potential rigor, experimental tractability, and enterprise deployment constraints. This is preparation for the role’s autonomy, research-roadmap contribution, and need to balance theory with practical impact.

Skills to revise

Python research prototyping
Revision exercise: Implement a small, clearly scoped research prototype in Python using PyTorch or JAX, and document its assumptions and limitations. Proficiency with Python and these ML stacks is required.
Reproducible experiment execution
Revision exercise: Run a small robustness or model-quality comparison, recording the setup, conditions, results, and how another researcher could repeat it. Designing and executing reproducible experiments is an explicit responsibility.
Proof-assistant practice
Optional preparation: Formalize a small lemma in one of Lean, Coq, Agda, or Isabelle and explain what was proved. Familiarity with proof assistants is preferred, not required.
Research presentation and critique
Revision exercise: Prepare a short research-style summary with a claim, method, evidence, limitations, and next question; explain it once for researchers and once for engineers. The role calls for peer-reviewed publication and clear communication to both audiences.
Engineering handoff
Revision exercise: Draft a handoff for a hypothetical research prototype specifying its validated behavior, unresolved risks, and constraints for production use, without assuming any particular Tenzin implementation. The posting calls for collaboration with ML engineering and infrastructure teams to validate and productionize research.

Your preparation checklist

Prepare a research contribution walkthrough
The role calls for a publication record or applied research impact. Select a relevant paper or industry result and prepare a brief account of the problem, your contribution, evidence, limitations, and next steps.
Work through a formal-methods design exercise
The role emphasizes verifiable, controllable AI and type-theoretic foundations. As interview preparation, sketch a proposed property, its assumptions, a proof strategy, and what the guarantee would not cover.
Draft a reproducible evaluation plan
The role requires experiments across safety, alignment, robustness, and model quality. Prepare a one-page plan with baselines, metrics, failure cases, and the artifacts needed to reproduce results.
Walk through a Python research prototype
The role requires Python prototyping with stacks such as PyTorch or JAX. Prepare a code or architecture walkthrough showing how an experiment is run, checked, and iterated; do not claim tools you have not used.
Explain a path from research to deployment
The role involves ML engineering and infrastructure collaboration on Tenzin and the Octave-X platform. Prepare an example or hypothetical handoff covering validation, engineering constraints, and the boundary between a research result and a production guarantee.
Practice communicating a research direction
The role includes presentations, roadmap contributions, and mentoring. Prepare a short explanation of one promising safe-AI question for researchers, then a version for engineers, noting trade-offs and how you would guide a junior researcher investigating it.
Bring focused questions for Octave-X
Ask how the team defines and tests Tenzin’s mathematical guarantees, prioritizes publication against product work, and transfers validated research into production. These are questions to clarify the role, not assumed company practices.

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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