Machine Learning (ML) Platform Engineer
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A Machine Learning (ML) Platform Engineer is an AI platform engineer who can perform ML Platform Engineering Tasks (for an in-house ML platform).
- Context:
- They can (often) collaborate with ML Engineers.
- They can (often) belong to an In-House ML Platform Team.
- …
- Example(s):
- a Netflix MLP Engineer.
- a AWS Sagemaker Engineer.
- ...
- See: Data Platform Engineer, In-House ML Platform Product Manager, GenAI Platform Engineer.
References
2021
- https://careers.twitter.com/en/work-for-twitter/202009/01ab05bf-f1ac-451d-a381-a6f2567b4508/4bd2f159-2157-4426-b99f-842a64670044.html/senior-machine-learning-platform-engineer.html
- QUOTE: We’re hiring several ML engineers across all ML Platform teams to help create an industry-leading ML Platform. If building better ML tools and 10x productivity increases excite you, give us a call. ...
- A passion for machine learning and developer tools.
- Motivated by delivering impactful products that accelerate our customers' workflows.
- An innovator with listening skills, empathy and a knack for discovering “product-market fit” for seed-stage ideas and delivering strong outcomes.
- You believe in software quality and set examples by writing robust interfaces, considering design principles and applying sound testing practices.
- A systematic approach toward project management and dealing with ambiguity (such as formulating and testing product hypotheses).
- A track record of shipping working software fast and reliably.
- You bring partners together across organizational and functional boundaries.
- You’re able to articulate a clear vision and enroll the team and partners into it, both in spoken and written form, while remaining open to a constructive dialogue.
- You multiply the effect of contributors by inspiring and growing them on and off the team across different levels of seniority, skills and geographical boundaries.
- Qualifications
- You have contributed to or working knowledge in three or more of the following:
- Open-source ML frameworks (e.g. Tensorflow, TFX, PyTorch)
- Cloud technology stacks (e.g. GCP or AWS and their product offerings)
- ML pipelines and their orchestration
- Jupyter notebooks
- Distributed data processing in Hadoop, Spark, BigQuery, or Apache Beam
- Modeling, model architecture or optimization
- Data and feature engineering
- Distributed training and/or GPU-based training and inference
- Experience with distributed run-time systems, their performance optimization and improving their resilience
- You have contributed to or working knowledge in three or more of the following:
- QUOTE: We’re hiring several ML engineers across all ML Platform teams to help create an industry-leading ML Platform. If building better ML tools and 10x productivity increases excite you, give us a call. ...