Found.dev
Role Highlights
Languages used
Key skills
AI
NLP
Information Retrieval
Machine Learning
Applied ML
Transfer Learning
Computer Science
Software Design
Data Processing
Data Structures
System Design
Technical Leadership
foundation model
Full Stack
Research
Testing
Architecture
Deployment
Debugging
BERT
T5
PhD
Multimodal
Modelling
Search
Storage
Security
UI
Infrastructure
Reliability
Cloud
Operations
Inference
pre-training
Tools, Libraries and Frameworks
Vertex AI
GCP
Mountain View
LOOP
Core ML
Description
The role involves defining and executing an applied research roadmap for a large user model ecosystem. The engineer will translate complex business goals into technical model formulations to improve user engagement and business metrics. Responsibilities include optimizing model performance through adaptation techniques while managing inference latency requirements. The position requires driving architectural improvements by establishing feedback loops with pre-training teams to influence data curation and model evaluation. Additionally, the engineer will design and productionize fine-tuning pipelines to convert state-of-the-art foundation models into domain-specific recommendation engines.
Required Qualifications and Skills
Candidates must possess a Bachelors degree or equivalent practical experience. The role requires 8 years of experience in software development, including 5 years in ML design and 5 years in launching software products. Applicants need 3 years of experience in software design and architecture, along with specific expertise in Transformer-based models. A Masters degree or PhD is preferred, as is experience in technical leadership and working within complex, matrixed organizations.
Disclaimer
Disclaimer: Job and company description information and some of the data fields may have been generated via GPT-4 summarisation and could contain inaccuracies. The full external job listing link should always be relied on for authoritative information.
About the company
Description
At gTech's Users and Products team, innovations are focused on enhancing user engagement and solving complex customer needs through technical expertise and a deep understanding of Google's and Alphabet's broad product environments. The team acts as a bridge between Google’s users and its product teams, ensuring that user insights are integrated into product offerings which support numerous annual product launches. The newly formed Machine Learning Data Operations (MLDO) team within gUP Operations plays a critical role in delivering and tuning machine learning and GenAI data operations across Google’s product suite, leveraging extensive global vendor networks. Google's overarching goal is to create impactful products and services, with gTech playing a strategic role in bringing these solutions to fruition through technological and operational expertise.
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