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MAchine Learning Engineer

Location: Glasgow (Hybrid)
Salary: Depending on experience
Hours: Full time
Placed On: 1 Feb 2024

Neuranics is a cutting-edge technology company specialising in magnetic sensing technology for metaverse and digital health applications. We are dedicated to leveraging the latest advancements in machine learning and artificial intelligence to solve complex problems and drive innovation in time series analysis.

We are therefore seeking a talented Machine Learning Engineer with expertise in real-time time series data analysis, signal processing, and machine learning. The ideal candidate will be responsible for designing, developing, and implementing machine learning algorithms and models to analyse and extract insights from large-scale time series data in real-time.

Primary Responsibilities

  • Collaborate with cross-functional teams to understand business requirements and identify opportunities for applying machine learning techniques to solve problems related to real-time time series data analysis.
  • Design and develop scalable and efficient machine learning algorithms and models to process and analyse large volumes of time series data in real-time.
  • Implement signal processing techniques and feature engineering methods to extract relevant information from raw time series data.
  • Evaluate and fine-tune machine learning models to achieve optimal performance in real-time applications, considering factors such as accuracy, latency, and resource utilisation.
  • Work closely with software engineers to deploy machine learning models into hardware, production environments and ensure scalability, reliability, and efficiency.
  • Monitor model performance in production and implement continuous improvements and optimisations to enhance performance and maintain reliability.
  • Stay updated on the latest advancements in machine learning, signal processing, and related technologies, and apply them to improve existing processes and develop innovative solutions.

Education And Experience

  • Bachelor’s degree or higher in Computer Science, Engineering, Mathematics, or a related field.
  • Strong background in machine learning, deep learning, and statistical modeling, with hands-on experience in developing and deploying machine learning models in real-world applications.
  • Proficiency in programming languages such as Python, R, or Java, along with experience using machine learning libraries and frameworks such as TensorFlow, PyTorch, scikit-learn, or Keras.
  • Solid understanding of signal processing techniques, time series analysis, and feature engineering methods.
  • Experience working with real-time data processing systems, streaming data platforms, and distributed computing technologies such as Apache Kafka, Apache Flink, or Apache Spark is a plus.
  • Strong analytical and problem-solving skills, with the ability to effectively communicate complex technical concepts to cross-functional teams.
  • Proven track record of delivering high-quality solutions in a fast-paced and collaborative environment.
  • Familiarity with containerisation technologies such as Docker.
  • Knowledge of DevOps practices and tools for building, deploying, and managing machine learning pipelines in production.

Personal Attributes & other requirements

  • Communicate with various departments to achieve common goals
  • Able to withstand high pressure situations
  • Willing to travel and on occasion

KPIs

  • Product ownership, initiative in proposing new products and features
  • All developments need to be treated as a safety critical application
  • Develop and deliver outstanding service to customers

What We Offer

  • An opportunity to change the world and work with a passionate team
  • Hybrid work environment in general
  • On-site gym;
  • Regularly organised social events;
  • Free coffee.

Multiple studies have found that a higher percentage of women and people from under-represented communities won’t apply if they don’t meet every listed qualification. Neuranics values candidates of all backgrounds. If you find yourself excited by our mission but you don’t check every box in the description, we encourage you to apply anyway!

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