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Aloe

Artificial Intelligence Researcher

Vancouver, BC, Canada

Full-time

Apply About employer

Applications open in a new tab.

Job details

Job type: Full-time
Apply by: May 1, 2027
Experience: 5+ years of applied research experience in industry labs or startups.
Posting language: English
Posted: Nov 2, 2026

Original job description

We’re looking for a Senior AI Researcher to bridge frontier research with product innovation. You’ll focus on advancing breakthroughs in AI and machine reasoning and translating them into applied systems that people use every day. You’ll help invent, prototype, and scale the core technologies that power Aloe. You will work in a close-knit team that spans philosophy, engineering, cognitive science, UX, and operations, in our shared mission to invent, implement, and deliver the most significant step change to AI since the introduction of ChatGPT.

Come close the imagination gap on how we build human-level AI and what it should be used for. Our team has unmatched clarity of purpose, and unmatched ambition. We seek to be the best in the world: on trust, capability, and ensuring the benefits of AI accrue to everyone.
Culture

We welcome teammates who want to do their life’s best work over the next 5 years. We are exceptional at what we do, and see ourselves as scientists, philosophers, and builders who enjoy working in the fellowship of other intrinsically motivated people. We strive to be Level 5 humans, co-creating a deliberately developmental atmosphere.

What you can expect working here to be like:

Speed: Our ambition will only be reached if we are laser focused on the essential and move with purpose. You will consistently find yourself not knowing how to proceed and needing to figure out how to take next steps despite that.
Opportunity: We are at an inflection point for our species and are bending the world toward one we want to live in – where everyone is safe and has access to the opportunities to express their innate creativity and pursue their deepest meaning.
Optimism: We are high fun, ultimately high agency.
Comfort with being uncomfortable: You should expect to always be in your “developmental zone,” which necessitates feeling inadequate and uncomfortable. Personal development is a core practice for continually leveling-up as technologist and as a human. Everyone is a senior person.
Support: We are all in this together, and here to help each other succeed. We’ve got empathy for each other because we’re all experiencing the discomfort of growth together. We’ll only win as a team, so the only valid use of ego here is to fuel our creativity with it.
Collaboration: We are building the rocketship as we’re flying it. We’re all responsible for finding ways to make ourselves and our team more effective. We’re run by ideas, not hierarchy.

What We Offer

The opportunity to see your work directly impact people’s lives at scale.
The chance to do the best work of your life surrounded by some of the most capable humans on the planet.
Compensation at the high end of the market for the role and company stage, including benefits.
Unceasing opportunities for career growth.

Responsibilities

Lead research projects that directly inform and improve product capabilities.
Develop and test novel approaches in areas like epistemic reasoning, risk, and abductive inference using both new and existing language models and various training methodologies.
Rapidly prototype and iterate on research ideas, with an eye toward production readiness.
Collaborate with engineering teams to integrate new models into live systems.
Mentor researchers and engineers, raising the bar for scientific and technical rigor throughout the org.
Track and adapt cutting-edge work from the research community into practical solutions.

Qualifications

Advanced degree in computer science, machine learning, or related field; requirement may also be met with equivalent research experience.
5+ years of applied research experience in industry labs or startups.
Proven ability to take ideas from concept to prototype to production.
Strong expertise in one or more areas: large language models, reasoning, reinforcement learning, self-supervised learning, or knowledge representation.
Skilled in Python and modern ML frameworks (PyTorch, JAX, TensorFlow).
Excellent communication and collaboration skills; comfortable working across disciplines.
Track record of impactful applied work (product launches, open-source contributions, patents, or widely used systems).

Preferred Qualifications

Experience with evaluation and benchmarking of applied ML systems.
Background in building human-in-the-loop, verification, or auto-escalation systems in AI.
Familiarity with safety, interpretability, or robustness in production models.
Experience mentoring applied research teams.

How to Apply

Send us a note at join@aloe.inc.

Apply About employer

More about this job

Responsibilities

Lead applied research projects that improve product capabilities, developing and testing novel reasoning approaches and rapidly prototyping ideas for production. Collaborate with engineering to integrate models into live systems, mentor colleagues, and translate cutting-edge research into practical solutions.

Requirements

Requires an advanced degree in computer science, machine learning, or a related field, or equivalent research experience, plus at least five years of applied research experience. Candidates should have expertise in one or more relevant AI areas, proficiency in Python and modern ML frameworks, a record of taking research into production, and strong communication and collaboration skills.

Skills

Applied AI Research
Large Language Models
Machine Reasoning
Epistemic Reasoning
Abductive Inference
Reinforcement Learning
Self-Supervised Learning
Knowledge Representation
Python
PyTorch
JAX
TensorFlow
Model Evaluation
AI Safety
Interpretability
Mentoring

Benefits

Benefits
Career Growth Opportunities

Visa sponsorship

Not detected in the job text

Keywords

Artificial Intelligence
Applied Research
Machine Learning
Large Language Models
Machine Reasoning
Epistemic Reasoning
Risk Modeling
Abductive Inference
Reinforcement Learning
Self-Supervised Learning
Knowledge Representation
Python
PyTorch
JAX
TensorFlow
Model Evaluation
Benchmarking
Human-in-the-Loop Systems
Verification Systems
Auto-Escalation Systems
AI Safety
Interpretability
Robustness
Production ML Systems
Prototyping
Mentoring

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