Sense Machine Learning Platform Lead Engineer? - SPG

Key Skills

Java Programming, Design, Management, Python Programming, SQL, Azure, AWS, Machine learning techniques, Data Mining, Deep Learning, Presto, Google Cloud Platform (GCP), Elasticsearch, SPARK Programming, Celery, Annotation, Large scale data processing, Data pipelines, Data Architecture, Apache Beam, Terabyte scale datasets, Dask - Python library

Job Description

The Sense Platform team builds specialized dataset, crowd annotation, and data-centric machine learning (ML) tools to enable cutting-edge, large scale deep learning development. Modern deep learning approaches are often limited only by the quality, complexity, and scale of datasets used to train models. Crowd data annotation tools, self-supervision, and data-centric ML approaches demand substantially more dynamic, flexible data platforms than traditional ML development.

You will play a key role in architecting, implementing, and scaling ML platform capabilities to achieve the best possible deep learning models for complex, multi-modal data. Platform components, services, and tools you build will improve data quality, crowd annotation efficiency, and precision targeting of data. You will also contribute to decisions around what data is most impactful to collect, annotate, and enrich using systems you build. Your work will make a direct, measurable impact on deep learning system performance on critical tasks.

Key Qualifications

• Solid software engineering skills in complex, multi-language cloud data processing environments. Fluency in Python.
• 4+ years experience working with cloud data processing technologies for both batch and streaming compute models (Spark, Dask, Celery, ElasticSearch, Presto, Apache Beam, etc.)
• 2+ years of hands-on experience building reliable, flexible data processing tools, services, platforms and pipelines in a scalable cloud environment (AWS, GCP, Azure, etc).
• 2+ years experience working as a tech lead on data platform projects. Includes building technical requirements, data model/architecture design, implementation, and clear communication across teams
• Ability to architect data processing systems and lead the team’s thinking on key technology and design choices
• Attention to detail and creative problem-solving: Do you dig into the data to troubleshoot a problem?
• Strong technical communication (both written and verbal), prioritization, and time management skills. Strong interpersonal skills to work both with your team and others.
• Thorough overall software development approach. You deliver clean, well-tested code.
• Experience building data systems for crowd data labeling approaches or human-in-the-loop ML systems (e.g. active learning, annotation quality measurement) is a plus
• Experience supporting data augmentation strategies or synthetic input data for ML training and evaluation is a plus

Description

You will lead design and implementation for key pieces of our machine learning and data annotation platform. This platform supports dataset management, crowd data annotation tools, and data enrichment using ML models. You will work directly with engineer stakeholders to understand platform use cases and deliver opinionated, purpose-built tools for ambitious, large scale deep learning systems.?

Your responsibilities will include:

• Propose and build platform improvements to balance quick delivery of data with scalable long term data architecture choices
• Design data models and multi-purpose cloud data architecture to support ML training, crowd annotation, and data mining use cases
• Lead technical projects to completion through hands-on work in scoping, prototyping, iterative development, and delivery phases of work
• Work closely with infrastructure and ML teams to lead key technology and design choices
• Collect requirements and engage stakeholders from different teams to bring into production complex cross-functional projects
• Collaborate across teams to drive adoption and usage of new capabilities you build
• Identify and evaluate new patterns and technologies to improve performance, maintainability and elegance of our data, annotation, and ML platform components
• Contribute to the technical roadmap of our data platform and pipelines
• Mentor fellow engineers in your areas of expertise
• Help grow a team culture that values effective collaboration, innovation, and technical perfection

Education & Experience

• Bachelors, Masters, or PhD Degree in Computer Science or equivalent professional experience