Personalization and Continual Learning Scientist

HushOne, Inc.; Kirkland (WA); Remote; USD 150,000 - 230,000; Full time

Benefits offered by this job

Stock options

401(k) with company match

Health, dental, vision insurance

Gym membership

Job summary

HushOne, Inc. seeks a researcher to design privacy‑preserving personalization systems that update user preferences without exposing other household data. You will drive experiments with held‑out evaluation, document deletion limits, and differentiate memorization from generalisation.

You will also develop forgetting mechanisms, evaluate personalisation without big pooled data, and contribute to continual learning and responsible AI initiatives within a remote‑friendly, hybrid team.

Qualifications

• Machine-learning research expertise with careful reasoning about privacy, causality and evaluation.
• Ability to distinguish memorisation from useful generalisation, and to walk through it.
• Design for forgetting and revision as first-class operations.
• Ability to evaluate personalisation without a large pooled dataset.

Responsibilities

• Design experiments to update user preferences when data changes without exposing other household members.
• Deliver personalization experiments with held-out evaluation and documented deletion limitations.
• Explain and reason about privacy, causality and evaluation in the design and assessment process.

Skills

ML research

Privacy & causality

Experiment design

Evaluation without big data

Job description

You help a private agent learn what matters to its user while preserving their control and their right to change their mind. The distinction that governs the work: memorisation is not personalisation, and a system that cannot forget is not one a person actually controls.

The work:

Research adaptation, memory selection, preference learning and resistance to forgetting or poisoning. Compare retrieval, explicit settings and parameter updates rather than assuming training is always necessary. Test what can be removed from memory and what model-level removal can actually guarantee.

What good looks like:

In your first 90 days, deliver a personalization experiment with held-out evaluation, rollback and documented deletion limitations.

Evidence we look for:

Bring machine-learning research expertise and careful reasoning about privacy, causality and evaluation. Show work that distinguishes memorization from useful generalization.

Required:

• Machine-learning research expertise with careful reasoning about privacy, causality and evaluation
• Work that distinguishes memorisation from useful generalisation, which you can walk through
• You design for forgetting and revision as first-class operations
• You can evaluate personalisation without a large pooled dataset

Nice to have:

• Continual learning or catastrophic forgetting research
• Causal inference
• Recommender systems with a privacy constraint

The exercise:

Design an experiment where a user's preferences change and the system must update without exposing another household member's data.

How we work:

We work together in the office, five days a week, and you can be based at any of our garages: Kirkland Garage (Kirkland, WA); UAE Garage (Dubai, Dubai). We hire across the United States, India and the UAE. We are remote-friendly around family: if you need to work from home some days to look after the people you love, we arrange that with you one person at a time, and we encourage people to use it rather than tough it out.

Pay:

We publish what we pay. Indicative ranges by market and level are on our compensation page, and they are realistic going market rates rather than headline numbers. Wherever we hire we pay at least the local market rate, and for full-time roles our floor is a living wage, never the statutory minimum. Compensation is reviewed every year and on promotion.

Equity:

Every full-time teammate gets stock options. Four-year vesting with a one-year cliff, sized to role, level and impact, and confirmed in writing at offer. High performers earn refresh grants.

Bonus and commission:

An annual performance bonus tied to clear company and personal goals, indicatively 10 to 20 percent of base for non-sales roles. Customer-facing roles carry on-target earnings, typically a 50/50 split of base and variable, with uncapped commission and accelerators above quota.

Health and peace of mind:

Medical, dental and vision for you and your family, plus life and disability cover. We have chosen the highest plan tier available to us rather than the cheapest one that clears the bar, because the point of this is that you never have to think about it. The specific plan numbers are confirmed in your offer letter.

401(k) with company matching:

A 401(k) with a company match, so the years you spend here compound into something that is yours whatever happens next. The match formula is confirmed in your offer letter.

AI tokens:

A budget of AI tokens of your own, because a company that says you should own your AI cannot be the company that rations it. Use them on the work and on whatever you are curious about.

Gym, and the everyday things:

A gym membership, and a corporate benefits programme with its own app, where you redeem real discounts with a long list of retailers on the ordinary purchases of a life.

Family:

We are remote-friendly around your family, arranged one person at a time, and we would rather you took it than toughed it out.

Referrals:

Refer someone we hire full-time who stays a year and you get $1,000 plus $10,000 in referral stock-based equity, on top of your own package.