Relevance AI

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Senior Machine Learning Engineer - Customer Success

  • Software Development
  • Full-time
  • Sydney, AU
  • Remote friendly

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 Senior Machine Learning Engineer to work with our clients, enabling them to solve complex data problems with 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:

  • Understanding the clients needs and work with them to illustrate and integrate Relevance AI to generate real business value for them.

  • Build and show demos and proof-of-concept using the platform.

  • Analyze and preprocess raw data: assessing quality, cleansing, structuring for ingestion into Relevance AI.

  • Collaborate with engineering team to bring your customer learnings to inform the direction of future product feature development.

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

  • Write and blog about how the platform can be used.

Qualifications:

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

  • At least 3 years of hands-on experience in using Python for Data Science with projects and outcomes to show for it.

  • Experience working with external clients.

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

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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Remote restrictions

  • Workday must overlap by at least 5 hours with Sydney NSW, Australia