Relevance AI

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Senior Machine Learning Engineer - Fast Inference

  • 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 productionise and speed up the inference models 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:

  • Deploy and design the architecture of our inference infastructure to create vectors/deep learning embeddings

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

  • 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 2 years of hands-on experience in using deploying fast machine learning models using combination of TensorRT, Onnx, TensorFlow Serving, Pytorch TorchServe, CUDA or TensorflowJs 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 Quantisation or Distillation

  • 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.

Remote restrictions

  • Workday must overlap by at least 4 hours with Sydney NSW, Australia