Interview Kickstart | Machine Learning Engineer

5 hours ago

pune, India Interview Kickstart Full time

Who We Are:

Interviews can be hard, especially at top tech companies like Google, Facebook, and Netflix. Many candidates fall short simply because they aren’t adequately prepared. That’s where we come in. Our acclaimed courses specialize in interview preparation and transitioning into high-demand tech fields such as AI, ML, and Data Science. At Interview Kickstart, current and former hiring managers have guided over 17,000 tech professionals through transformative career journeys, ensuring their success in landing coveted positions. Think of us as “the everything store” for career transitions and interview skill development.

How Do We Do That, You Ask?

We have a structured approach to interview success, which includes:

  • Career Accelerator Course
  • Comprehensive end-to-end courses and platform
  • A roster of over 600+ instructors from leading Silicon Valley companies like Google, Facebook, Amazon, and Netflix
  • A holistic approach that includes live classes, mock interviews, personalized coaching, resume refinement, career strategies, and invaluable referrals

What’s more exciting is that we are completely remote and hiring the best people we can find regardless of geography.


Role Overview:

As a Machine Learning Engineer at Interview Kickstart, you will play a crucial role in developing and deploying cutting-edge AI/ML solutions that enhance our platform and improve the learning experience for our students. You will work closely with data scientists, engineers, and product managers to build and scale robust and impactful machine learning models.


Responsibilities

  • Research, design, and develop machine learning models for various applications within the Interview Kickstart platform, such as:
  • Personalized learning paths: Recommending optimal learning paths for individual students based on their skills, career goals, and learning progress.
  • Predictive analytics: Forecasting student success, identifying potential drop-off points, and proactively addressing student challenges.
  • Content optimization: Analyzing student engagement data to optimize the content delivery and presentation of our courses.
  • Fraud detection: Developing models to identify and prevent fraudulent activities on the platform.
  • Train and evaluate machine learning models on large datasets using a variety of techniques, including deep learning, natural language processing, and time series analysis.
  • Develop and maintain robust and scalable machine learning pipelines, including data preprocessing, feature engineering, model training, and deployment.
  • Collaborate with cross-functional teams (engineering, product, design) to integrate machine learning models seamlessly into the Interview Kickstart platform.
  • Conduct research and stay up-to-date on the latest advancements in machine learning and apply them to improve our products and services.
  • Contribute to the development of best practices for machine learning development and deployment within the company.
  • Mentor junior engineers and contribute to the growth of the engineering team.


Qualifications

  • Master's or Ph.D. in Computer Science, Statistics, or a related field.
  • 2+ years of experience in developing and deploying machine learning models in a production environment.
  • Strong programming skills in Python and experience with relevant libraries (e.g., TensorFlow, PyTorch, scikit-learn).
  • Experience with cloud platforms (e.g., AWS, GCP, Azure) and cloud-based machine learning services.
  • Experience with agile development methodologies.
  • Excellent communication and collaboration skills with a strong ability to explain complex technical concepts to non-technical audiences.
  • Strong problem-solving and analytical skills with a data-driven approach to decision-making.
  • Passion for machine learning and a strong desire to build innovative and impactful products.


What would Excite Us ?

  • Experience with specific machine learning techniques (e.g., deep learning, reinforcement learning, natural language processing).
  • Experience with big data technologies (e.g., Hadoop, Spark).
  • Experience with containerization technologies (e.g., Docker, Kubernetes).
  • Experience in SAAS based / scaled Product based organisations

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