At Liftoff, we’re solving one of the core problems faced by every mobile app: growth. To do so, we build Machine Learning models and infrastructure that can accurately predict which apps a user will like and how to connect them in a compelling way. We work with small startups getting off the ground right up to large companies like Amazon, Airbnb and Nike. Our systems operate at a scale unseen outside of the largest Internet companies — processing over 2M requests per second and interacting with over a billion users. Our technology is creative and we have strong product-market fit; as a result, we've already reached profitability and are growing exponentially.
Infra Software Engineer Redwood City, CA, United States, San Francisco, CA, United States, or Silicon Valley, CA, United States
Machine Learning Engineer San Francisco, CA, United States or Silicon Valley, CA, United States
We have a unique breadth and depth of technical challenges to work on here including distributed systems and high performance computing challenges. We're touching 1 billion users every week, which gives us a huge room for experimentation and working on real time personalization of applications to each user.
User growth is a fundamental problem for every mobile application developer in the world and we count some of the largest in the world as our customers e.g. Amazon, Uber, HBO and Twitter.
We have a lot of success in the market. We've built very substantial revenues, we won't need to raise more funding as we've hit profitability.
We think deeply about our engineering culture and optimize for hiring smart generalists. You can check out our engineering culture page to get a deeper insight into how we think about this: http://liftoff.io/company/engineering/
Our founding and engineering team is very strong. Our co-founder Phil co-created the popular browser extensions, Vimium. Every co-founder has founded previous startups and we've had near-zero employee churn since we founded the company.
We are a small engineering team working at the intersection of high-performance distributed systems, machine learning, and programmatic web UX, demanding creativity, constant learning, and raw smarts.
Our engineering team gets to work on exciting large-scale projects like building Machine Learning models and Big Data-driven technology that can accurately predict which mobile apps a user will like, and connect them in a compelling way.
The systems we build operate at a scale unseen outside of the largest internet companies — processing over a million requests per second and interacting with billions of users.
Our Engineering Philosophy
1) Use the best tool for the job: Whether it’s robust open source data stores, high-leverage languages (e.g., Clojure and Go), or building our own CLI tools, we use the technology that will result in the best long-term product.
2) Tighten feedback loops: We leverage fast integration tests, one-command deploys, and prod AB tests that reach significance in minutes so that we can learn and improve our systems faster.
3) End-to-end ownership: Engineers own projects from design to rollout, which decreases overhead, miscommunication, and the risk of building the wrong solution–and it’s just more satisfying.
4) Invest in tooling: We spend about 20% of our time building advanced tooling and dashboards that add a multiplier to our future effectiveness.
5) Constant learning: We choose projects that maximize learning, not just business impact.
6) Automate everything: QA is replaced with high-test coverage, sysadmin with automated deploys, operations with high-signal alerting, etc.
1) We're integrated with companies via our mobile SDK's. Our servers are fielding over 2M requests per second and our ML prediction intelligence currently process 100M predictions a second . For every request, we're running ensembles of bidding algorithms, distributed data stores and Machine Learning models to figure out how valuable each potential new user is to a developer. Doing this with low latency is very hard.
2) We like smart engineers who are interested in machine learning, even if you don't have experience with it. At Liftoff you'll work alongside machine learning experts and be able to learn about it quickly. Applying machine learning to real world problems is a particularly great skill to acquire right now as an engineer.
Product recommendation system - Users can understand an Ecommerce app much better when shown a product they can buy there. Using the large dataset Liftoff has access to and our machine learning knowhow, we can build an effective recommendation system for those products.
Custom distributed columnar database - A specialized in-house data store to house the hundreds of terabytes of high dimensional distilled data Liftoff processes, and serve it in real-time.
Full stack AB test dashboard - Liftoff has enough scale to run hundreds of AB tests daily on everything from mobile UX to ML algorithms, and get useful results within hours. To support that scale of testing, we need great tooling for the AB test workflow, including strong data visualization and analysis UXs.
We have a supportive, inclusive, and data-driven culture where we advocate for and celebrate values such as transparency, humility, courage to change, and proactivity.
Wellness stipend that can go to whatever program employee chooses to spend it on related to wellness/happiness.
We offer career/personal development stipend that can be accessed any time.
100% covered by Liftoff
Commuter perks (parking pass, CalTrain GoPass, Lyft credits, etc)
Including an Employer Matching program (up to 3% of total employee contribution).
If commute is at least 45min, employees get to work from home on Thursdays (which a good number of us take advantage of!).
Relocation assistance available!
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