Work from Office
Any Post Graduation
You would be joining our team as a Machine Learning Engineer to ship product improvements involving ML, from understanding the data to implementing the solution and deploying it in production. You will work as part of a cross-functional product team (product manager, ML engineer(s), ML platform engineer(s), backend, frontend, and QA) to directly have an impact on business objectives. ML engineers at MetaMap work on a range of topics, with a focus on computer vision: document classification, face recognition, video liveness detection, OCR, and document reading. We use PyTorch for our models and deploy our ML API using docker, Kubernetes, and rabbit MQ.
- Be a part of a cross-functional product team (2-week sprints), and deliver on business objectives by collaborating with the team.
- Play an active role in raising the excellence level of the team, coaching more junior team members.
- Identify pain points at the team or company level, either on the operational or business level, and set up tools, processes, and other initiatives to improve and solve the problems in the long term.
- Be involved in the full lifecycle of new ML feature development: understanding the existing data, building a quick prototype, testing it, implementing a robust production-ready change to our API, and deploying it to production (with an AB test) using internal tools.
- Come up with new ideas to improve the product.
- Solve bugs, and be able to investigate our ML pipeline to find root causes.
- Train machine learning models, compute metrics and monitor these models once deployed in production.
- Part of the job involves longer-term applied machine learning research, but always as a means to solve concrete product needs.
- Solve identity-related problems: document detection, document verification, OCR, face detection and recognition, face liveness detection, etc.
Skills & Experience
- Deep knowledge in machine learning; at least 5 years of experience working as a Machine Learning Engineer/Data Scientist or equivalent roles.
- Experience working with one of the main deep learning libraries (PyTorch, TensorFlow, etc. ).
- Some experience in computer vision, OpenCV, and natural language processing.
- Coding proficiency: python, git, testing, linting, etc.
- Data analysis skills to dive into the data and quantify ideas or bugs.
- Ability to train machine learning models, monitor metrics, and set up proper benchmarks.
- Experience collaborating with non-ML teams (product, backend, DevOps), and pushing cross-team initiatives.
- We value team members ready to take end-to-end ownership and help take the team and company to the next level.
- Experience pushing models in production.
- Experience with PyTorch.
- Experience in face recognition, OCR, and document reading.
- Experience with docker, Kubernetes.
- Experience building world-class APIs.