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Lead Machine Learning Engineer

At Relevance AI our mission is to accelerate developers to solve similarity and relevance problems through data, this enable important use cases such as recommendations, topic modelling, semantic search, zero-shot classification and more.

Relevance AI

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11-50

At Relevance AI our mission is to accelerate developers to solve similarity and relevance problems through data, this enable important use cases such as recommendations, topic modelling, semantic search, zero-shot classification and more.

Our first step towards helping teams solve similarity and relevance, we started with the data type that all the top tech companies use - Vectors, a high dimensional representation of data used to determine similarities between data, commonly produced through deep learning. 

We are looking for a Lead Machine Learning Engineer to develop and expand on our cutting-edge vector platform. You will be joining a rapidly growing team backed by Insight Partners (investor in Monday.com, Twitter, etc) where new ideas and state of the art Machine Learning is applied daily.

Responsibilities:

  • Design accurate and scalable algorithms for creating, storing, evaluating, searching or analysing vectors/deep learning embeddings

  • Analyze and preprocess raw data: assessing quality, cleansing, structuring for downstream processing

  • Collaborate with engineering team to bring your research and prototypes to production

  • Be involved in all aspects of a project life cycle, work with clients to illustrate and integrate Vector AI to generate real business value for them

  • Self starter, take ownership of their work and the quality of it.

Qualifications:

  • Degree or equivalent experience in quantative field (Statistics, Mathematics, Computer Science, Engineering, etc.)

  • At least 5 years of hands-on experience in using Python for Data Science with projects and outcomes to show for it

  • Understanding of Vectors/Deep Learning embeddings and have experience in utilising them for search, recommendations, personalisation, etc

  • Deep understanding of training Deep Learning models in either Pytorch or Tensorflow (including Convolutional Neural Networks, LSTM, Transformers, Autoencoders, etc)

  • Deep understanding of traditional statistical modeling: clustering, dimensionality reduction, K-nearest neighbors

Bonus Qualificaitons:

  • Familiarity or Experience with Docker, Kubernetes, Kafka, Spark, Elasticsearch, MongoDB, Lucene, SQL or Plotly

  • Familiarity or Experience with python libraries of: FastAPI, FAISS, RAPIDS, nmslib, Dask

  • Speciality in a specific field of machine learning: Computer Vision, Time Series, Natural Language Processing, Audio, Clustering, etc

  • Strong familiarity with a certain industry where vectors are or can be applied to.

Apply now to be an early journey of a startup that will empower data science and developers with the tooling they deserve.

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About Relevance AI

Learn about Relevance AI and their company culture.

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We give shape to your qualitative data, making it simple to discover and understand your data's meaning.

Our vision is to give shape to the world’s qualitative data, making it simple to discover and understand.

80% of data is qualitative, in the form of text, images, audio or video.

Using the power of AI vectors, we can reveal the semantics of qualitative data with minimal labelling or training, making it easier and more affordable for businesses of any size.

Our AI-first semantic search product enables any business to have Google-like search functionality on their website, app or enterprise software solution without the need to code or configure.

That's just one small but powerful application of what our tech can do. Talk to us about what we can do together to unlock the value of your qualitative data.

What we do

Relevance AI is a machine learning SaaS startup building a platform to help companies and developers leverage machine learning vectors to extract business value from qualitative data such as text, images, audio, pdfs, etc. This involves managing the lifecycle of vectors such as creating, storing, evaluating, searching and analysing them.

80% of business data is qualitative and unstructured. Top companies like Spotify, TikTok, Google utilise qualitative data to create the most personalised and successful products. We want to democratise the access of that 80% for businesses to build the most powerful applications such as NLP Search, Visual Recommendations and help decision makers get a 360 view of their data.

Tech stack

Learn about the technology and tools that Relevance AI uses.

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