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Senior Software Engineer, Data Mining Infrastructure

Motional

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Job Summary


Job Type
Permanent

Seniority
Senior

Years of Experience
At least 4 years

Tech Stacks
Analytics
Python
AWS

Job Description


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The Data Mining team in Singapore develops cutting edge algorithms and infrastructure for data mining and data verification in the context of autonomous driving. Our goal is to improve the on-road performance of products that use machine learning at their core by mining for errors, anomalies & long-tail events. We are looking for engineers passionate about autonomous driving technology and its potential impact to the world.

Motional's Machine Learning team has produced groundbreaking advancements in the autonomous vehicle industry including nuPlan (https://arxiv.org/abs/2106.11810), nuScenes (https://www.nuscenes.org), PointPillars (https://arxiv.org/abs/1812.05784), and PointPainting (http://arxiv.org/abs/1911.10150).

What you'll be doing:
  • Work on large-scale solutions for mining challenging driving scenarios which help boost model performance across various autonomous driving tasks
  • Collaborate with ML infrastructure teams and contribute to automated framework for continuous learning through iterative mining, training and deployment
  • Build dashboards around data verification use cases to maximize cost efficiency and throughput of data annotation pipeline
  • Develop distributed large-scale pipelines and optimize internal tools & open-source libraries to run efficiently and robustly at production level

What we’re looking for:
  • MS in Computer Science, related quantitative field or equivalent experience
  • Experience architecting and deploying large-scale systems
  • Experience with tools for data manipulation, storage and visualization
  • Experience with dashboard building libraries for data analytics
  • Experience with core AWS services or similar cloud platforms
  • Experience with writing production-quality code in Python
  • At least 4 years of software development experience

Bonus points:
  • Experience with machine learning or statistical analysis
  • Experience with building infrastructure for machine learning
  • Experience with distributed systems design and architecture
  • Experience with high-performance system optimization and deployment

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