Machine Learning Engineer
2 weeks ago
It’s an amazing time to join Pocket FM as we continue shaping the future of entertainment globally with audio series. With a strong repository of audio series, Pocket FM has evolved as the powerhouse of entertainment.
Our mission is to give wings and wisdom to stories, transforming them into a strong entertainment package. Pocket FM has grown to 130 million listeners community, spending an average user time of over 115 minutes every day, while the total streaming on the app counts to over 6.5 billion minutes monthly.
Summary
We are seeking skilled and experienced ML Engineers to join our team. As an ML Engineer, you will play a crucial role in developing and scaling up our recommendation systems. You will leverage your expertise in ML Engineering to ensure that our workflows are efficient, our ML training algorithms are optimized for training and inference. You will work very closely with the data scientists on the team and also collaborate with back-end teams, content teams, product and engineering teams and will be responsible for driving impact. Our team is focused on the following sub-areas:
Recommendation Systems: Improve the homepage user experience, autoplay experience using state-of-the-art recommendation techniques and by discovering problems specific to the PocketFM platform and solving them.
Content Analysis: Utilize advanced data analysis techniques and machine learning algorithms to analyze the performance of existing content and identify trends and patterns that contribute to higher retention and engagement rates.
Predictive Modeling: Develop predictive models that can accurately forecast the potential impact of new content releases on user behavior, retention, and satisfaction and use them for content and recommendation use cases.
Content Optimization: Collaborate with content creators, product managers, and marketing teams to optimize the content release strategy. Recommend appropriate content types, formats, and timing to maximize user engagement.
For the areas identified above, you will play a crucial role as an ML engineer with the following responsibilities:
Develop tools and features for improved efficiency of data scientists and research scientists on the team.
Improve the efficiency of our existing workflows and pipelines by developing feature stores and by minimizing any redundancy between various workflows. Re Architect the entire design where appropriate.
Closely work with the data engineering team to elucidate the data requirements for efficient recommendation pipelines.
Develop tools to monitor feature drift, failure of pipelines and setup alerts so that we are proactively monitoring all our workflows.
Come up with strategies and guidelines to speed up ML training and inference of workflows, reduce the memory footprint of the workflows. Dig deep into ML training itself and improve the training algorithm efficiency by optimizing the code/infra on which the model is trained such as from CPU to GPU etc.
Qualifications:
Education: A BTech or MTech degree in Computer Science, Data Science, Statistics, Mathematics, or a related field.
Experience: Minimum of 3 years of experience with a focus on ML Engineering.
Big Data Tools: Familiarity with big data processing tools like Hadoop, Spark, SQL or similar. Familiarity with AWS/Databricks or another cloud environment is required.
Programming Skills: Proficiency in programming languages such as Python, R, or similar for data analysis, manipulation, and modeling.
Good working knowledge with orchestration tools such as MLFlow, Airflow, databricks etc.
Machine Learning Expertise:We look for strong ML foundations both in traditional ML methods such as logistic regression, gradient boosted decision trees as well as neural networks. Experience with deep learning frameworks such as tensorflow, pytorch, and other ML tools such as scikitlearn, XGBoost etc. Experience with tensorflow/pytorch and experience in using GPU for speeding up machine learning training in tensorflow/pytorch. Familiarity with tools like Ray for efficient and parallel training and inference.
Communication Skills: Excellent communication and presentation skills, with the ability to effectively convey complex data-driven insights to both technical and non-technical stakeholders.
Analytical Thinking: Strong problem-solving skills and a keen eye for detail, with the ability to draw meaningful conclusions from data.
Team Player: Demonstrated ability to work collaboratively in a team-oriented environment.
Passion for Content and User Experience: A genuine interest in content creation, user experience, and understanding what engages and delights users.
Preferred Additional Skills:
Experience in the entertainment, media, or digital content industry is a plus.
Join our team and help shape an outstanding content experience for our users. If you are passionate about recommender systems, content analytics, machine learning, we would love to hear from you. Apply now and be part of our exciting journey towards content excellence
Location - Remote
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