At GoodOnes, we are on a mission to liberate memories from cluttered camera rolls.

We’re rethinking the entire personal photo/video experience from the ground up. Current photo systems (Google/Apple) encourage just piling up photos and videos — most of us spend more time taking photos than looking at them.

We’re solving this through a unique combination of AI + HI (artificial + human intelligence) that allows everyone to curate and edit their personal media in a scalable and manageable way, and using the curated media to create the “Netflix for your memories”. We’re letting every human be the director in their life’s documentary. And it’s all fully compatible with Google and Apple photos.

Founder/CEO is former PM leader in Google Photos monetization and managed the consumer PM team at Dropbox. We’re venture backed with top-tier VCs, and are looking to hire ambitious folks who are looking to change the way humanity interacts with their memories.

You will be the founding ML engineer for GoodOnes, working directly with the founder (former leads/execs in Google, Dropbox) and world-class peers (Uber) who are currently ML hobbyists or are pursing a masters degree in ML. This is will be a key and very pivotal role that will define our ML architecture and engineering direction for a long time at GoodOnes.

We want a ML engineer, not a ML scientist who isn’t comfortable with developing within our iOS app directly in Swift and would throw a model over the wall trained with a few thousand lines of Python.

We currently want to create CoreML models that are fine-tuned on the device using the data our users generate as they interact with the app, especially with our explicit like / archive mechanism.

Our goal is to create a personalized ML model of what our users actually like based on their training actions as part of interacting with our app as part of a private device only design.

Current Apple & Google photos generalized models do not do a good job currently, highlighting you waiting at the line as the “highlight” photo for an event and we believe with our app we can solve this puzzle while staying private.

We currently use Apple’s built in vision library to help with our current auto-filtering system and are developing a basic model that updates on the phone, but it is currently at an MVP level that we want a dedicated ML engineer to take to the next level.

All of the above can change also, based on the input you would give in designing this ML system, including possibly not using CoreML in the end. This will be something you own and is our current rough draft.

Native Apps: Unlike many startups at this stage, we strongly believe in creating a quality native platform app leveraging everything that the native platform can give us. We are not a react native or cross platform shop. Eventually when the app has proven itself in the iOS realm, we would create an Android native equivalent.

Hybrid Work: We are a hybrid distributed team, with everyone within the PST to EST time zone band. The CEO, CTO, UX designer and a couple others are within SF and we have a dedicated office too.

The Best Tools: We use Linear for task tracking, Notion for our wiki, SwiftUI to build our app and a plethora of SaaS services such as Sentry. We will give you a new M2 laptop to develop on and a personal CUDA workstation if necessary. We will never use JIRA and if you think we can improve by using something better, we are all ears.

Autonomy Focused: We believe in growing autonomous, independent engineers and loathe micromangement. We highly value ideas and feedback and ask everyone to input their product and engineering ideas.