Entrupy aims to establish itself as the global standard for authenticating physical goods. We enable businesses and consumers to instantly verify the authenticity of high-valued physical objects, starting with luxury goods (e.g. leather goods and accessories). Our mission is to protect businesses and consumers from transacting in counterfeit goods. Currently in use by thousands of secondary resellers and marketplaces worldwide, Entrupy provides the only scalable technology capable of authenticating high-value products.
Why join us?
Over the past year our revenue and authentication volumes have doubled and our customer base has grown by 50% with minimal churn. We are scaling up and experiencing a lot of growth. We've run close to cashflow positive for a long time, opting to invest in growing our current products and expanding into new areas.
We are the first mover and largest player in providing systemized and automated authentication solutions in the luxury space. This is an intricate problem both technologically and operationally, and we're uniquely positioned to bring our insights to other verticals such as sneakers, watches and collectables.
Two of our investors are quite well known: Yann LeCun (VP and Chief AI Scientist at Facebook, ACM Turing award), Zach Coelius (early investor in Cruise Automation which sold to GM for $1B)
We still have one foot in academia and always work on cutting-edge machine learning and computer vision.
Engineering at Entrupy
Our engineering team is based in New York and has seven members who work on mobile apps, backend services, infrastructure and internal tools. We also have a small data science team with members in India and Brazil. Engineers take ownership of projects and system areas and collaborate across the entire company. As an early engineer on a small team, you'll have the opportunity to work on a wide variety of challenges and projects and define important pieces of a product that can't be found anywhere else.
In 2022 we're aiming to grow the engineering team by 2-3x. Different project areas will have team sizes of between one to four engineers. As teams grow they'll adopt processes and tools that make sense for them.
Entrupy's technology enables authentication and fingerprinting of an ever-expanding catalog of luxury goods. Users take microscopic images of different areas for each item with our mobile app and lens attachment. Machine learning systems run a battery of in-depth checks, assessing the relevant properties of each item to determine its authenticity.
Core areas include the following:
Machine learning: All of our products are powered by ML systems capable of authenticating a massive variety of products. Doing this requires deep analysis and research, and iterative development of targeted tests that deeply understand each item. These systems need to be robust to real-world use and misuse by users across a range of industries and contexts, and their output needs to be comprehensible to a non-technical audience. These products are supported by an internal platform capable of handling both live production requests and training fully auditable, complex models from diverse data sources. Specialized interfaces and tools allow easy handoff between data science teams and production infrastructure. Common services related to annotation, dataset management, job monitoring and product analytics enable rapid development, deployment and iteration.
Mobile apps: We have a wide range of customers from small pawn shops and resellers to high-volume enterprises. Our apps need to cater to different workflows and be intuitive to users with limited knowledge of luxury goods. Real-time capture guidance and integrated support communication are important pieces to create a high quality experience. These algorithms and corresponding user interfaces are complex and specific to our supported product lines and software/hardware stack; off-the-shelf solutions do not adequately solve capture problems.
Platforms and infrastructure: Our Vue.js dashboard platform and underlying real-time APIs power support, training, knowledge and analysis tools, enabling efficient and accurate human review and supporting our data teams in building domain knowledge. Internal services such as search, reporting and job routing layers power a variety of other applications. Our analytics infrastructure and software suite allows a wide set of teams to understand all aspects of our data and product. Heavy automation and careful service design allow a small team to support many use cases.
Scale up machine learning products. How responsive can models and systems be to both increases in data for a single product and additions to product lines? What's the best way to quickly get improvements from research into the hands of users? How can we continuously monitor and improve systems in the wild?
Improve support tooling and knowledge platforms. What's the most relevant information to describe an item? How can annotation be framed so that other teams and automated systems share understanding?
Build high-availability API and data platforms that can scale and serve a variety of use cases. What types of systems balance short-term needs with longer term predictions?
Figure out common reasons for items to require manual review, then work cross-department to discover and implement solutions. Many problems can be approached from client, backend, ML, support and customer training angles: what's the best way to drive improvements holistically?
Understand the boundary between variation due to user behavior, hardware variance, usage patterns and differing real-world objects. What are the different ways people can use the system? The range of counterfeits is essentially infinite: what should be tested for?
Working at Entrupy
- We act with high professional standards and a sense of integrity
- We love taking calculated risks, and do not fear making mistakes quickly and cheaply (but seek to avoid making the same mistake twice)
- We actively foster an environment for our talent to thrive
- We look at the root cause and come up with scalable, long term solutions
- We openly support and promote our team members’ differences and similarities
- We encourage open collaboration and communication across our teams
20 days/year PTO
Core hours are 12 - 5 EST: some of our engineers start earlier, others work later.
Work from Home
We're currently remote and plan on relocating to our NYC office sometime this year. Historically there's been flexibility, we expect team members to show up at the office ~3-4 times per week.
Interested in this company?
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