Machine Learning Engineer

Key Skills

C++, Data science techniques, Design, Git, Java Programming, Machine learning techniques, Python Programming, PyTorch, TensorFlow

Job Description

About the team:

There are 8 billion interactions a day by people with our recommendations. That’s 5 million a minute! We see the success of our models and experiments astonishingly quickly. We live in what might be the fastest model-learning playgrounds in the world. We have built an infrastructure that enables model deployment at scale and speed. Our Machine Learning Engineers work closely with the rest of the data science and engineering teams to build models using the best-in-class AI tools and make the most of our large heterogenous data. This is a highly fertile environment for building, experimenting, refining and affecting real change from computational advertising models where the impact of our work becomes visible incredibly fast.

The Experience You'll Need:

To succeed in the Machine Learning Engineer role at InMobi, you will need a strong background in hands-on coding for training and deployment of ML/AI models and be capable of dealing with large scale data. You need to be well-versed with Deep Learning, and have a good understanding of modern DL techniques such as transformers, LLMs, etc.

Required:

• Education: Bachelor's degree or higher in Computer Science, Engineering, Mathematics, or a related field. A Master's or Ph.D. in a relevant discipline is preferred.
• Experience: 6+ years of experience working as a Machine Learning Engineer or in a similar role. Demonstrated experience in designing, developing, and deploying machine learning models and algorithms. You should have created, stored, and retrieved embeddings in the most efficient manner, trained and deployed Deep Learning models, and run inference at scale.
• Strong programming skills: Proficiency in programming languages such as Python, Java, or C++. Experience with machine learning libraries and frameworks (e.g., TensorFlow, PyTorch, scikit-learn) is required. Familiarity with Microsoft Azure is a plus.
• Solid understanding of machine learning techniques: Strong theoretical and practical knowledge of machine learning algorithms, including supervised and unsupervised learning, deep learning, reinforcement learning, and natural language processing.
• Data handling and preprocessing: Experience with data wrangling, cleaning, and preprocessing techniques. Proficiency in PySpark and working with large datasets is desirable.
• Software engineering skills: Familiarity with software development practices and version control systems (e.g., Git). Ability to write clean, modular, and maintainable code.
• Problem-solving mindset: Strong analytical and problem-solving skills to tackle complex data-driven challenges. Ability to break down complex problems into manageable tasks and propose effective solutions.
• Communication and teamwork: Excellent written and verbal communication skills. Ability to work collaboratively in a team environment, effectively communicate technical concepts to non-technical stakeholders.
• Experience in applying Data Science in digital advertising is a big plus

The Impact You'll Make:

As a Machine Learning Engineer at InMobi, you will:

• Design, develop, and implement machine learning models and algorithms that address specific challenges in a business at InMobi. This includes data preprocessing, feature engineering, model training, validation, and deployment.
• Work closely with data scientists, data engineers, and domain experts to collect, clean, and analyze large datasets. Perform exploratory data analysis to gain insights and identify patterns or trends that can be leveraged for machine learning tasks.
• Transform raw data into meaningful features that can be used by machine learning algorithms. Select and engineer appropriate features to enhance model performance and interpretability.
• Develop and implement training pipelines to train machine learning models using state-of-the-art techniques and frameworks. Evaluate model performance using appropriate metrics and fine-tune models to achieve desired results.
• Collaborate with software engineers to deploy machine learning models into production environments. Ensure seamless integration of models within existing systems and infrastructure.
• Stay up to date with the latest advancements in machine learning and related fields. Proactively identify areas for improvement and propose innovative solutions to enhance model performance, scalability, and efficiency.
• Collaborate effectively with cross-functional teams, including data scientists, software engineers, and business stakeholders, to understand requirements, define project goals, and deliver high-quality machine learning solutions.