Machine Learning Research Engineer (Optimization)

Machine Learning Research Engineer (Optimization)

  • Location

    Boston, Massachusetts

  • Sector:

    AI & Machine Learning Research

  • Job type:


  • Salary:


  • Contact:

    Molly Boca

  • Contact email:


  • Job ref:


  • Published:

    4 months ago

  • Expiry date:


  • Consultant:


Machine Learning Research Engineer (Optimization)

We are currently seeking a Machine Learning Research Engineer (8-bit Quantization, Optimization) to join an innovative MIT spin-off that has dedicated its work to building technology that matters. As a Machine Learning Research Engineer in our company, you will join us in continuing our path of technological breakthroughs within modern-day computing through the use of Deep Learning applications in a way that has never been done before.

Located in the Greater Boston, this post series A startup is keen on bringing in talented researchers and scientists to aid in the production of high-performance computer hardware that is built with the intent of optimizing the speed and accuracy of the traditional computational architecture.

Our ideal ML Research Engineer would be a well-rounded individual with a background in Machine Learning Research and Software Engineering, ideally having experience working with quantization/pruning/model compression/model optimization.

We can offer our Machine Learning Research Engineer:

  • A chance to develop the next generation of computing architecture
  • A culture rooted in diversity that offers our employees the opportunity to work with some of the best scientists, engineers, and coders from around the globe
  • An extremely energetic work environment with a mature start-up feel
  • Expansive benefits coupled with the promise of exciting technical innovation

Key Skills: ML conferences, NeurIPS, NIPS, ICML, ICLR, CVPR, SysML, CPU, GPU, NN, Machine Learning, algorithms, models, training, optimization, computer science, CC++, C, Python, software engineering, data scientist, neutral network, 8 bit quantization, quantized models, quantization, AWS, Azure, Pyspark, Tensorflow, Pytorch, floating point, inference, low precision