Principal Machine Learning Engineer
Mission Lane210,000 - 256,000 USD/year
- Full Time
- Junior (1 to 2 years)
Not SpecifiedCompensation
Senior (5 to 8 years)Experience Level
Full TimeJob Type
UnknownVisa
Artificial Intelligence, AI & Machine Learning, Data Science, Software EngineeringIndustries
Requirements
Candidates should possess proven experience building large-scale Machine Learning systems in production environments, familiarity with tools such as Flink, Spark, PyTorch, TensorFlow, or similar frameworks, and proficiency in languages like Python, Java, or C++. Strong knowledge of industry best practices for deploying, maintaining, and scaling ML systems in production is essential, alongside domain expertise in areas like ad-tech, recommendation systems, or personalization.
Responsibilities
The Principal Machine Learning Architect will design and build scalable, reliable, and low-latency ML systems, ensuring they meet strict performance requirements and incorporating cutting-edge ML trends. They will collaborate closely with engineering, product, and data science teams to align technical designs with business priorities, establish best practices for maintaining model performance, and lead large-scale initiatives related to architecture transitions. Furthermore, they will provide technical leadership, mentorship, and evangelize the company’s ML technology while developing a deep understanding of business KPIs and ensuring ML systems deliver real-world impact.
Skills
Large-scale ML systems
Flink
Spark
PyTorch
TensorFlow
Python
Java
C++
Model monitoring
Drift detection
Retraining strategies
ML engineering trends
ML system deployment and scaling
Sift
Real-time fraud detection and prevention platform
About Sift
Sift provides a platform focused on detecting and preventing online fraud in real-time, catering to clients in e-commerce, fintech, and digital marketplaces. The platform uses machine learning and artificial intelligence to analyze large datasets, allowing it to identify fraudulent activities effectively. One of its standout features is dynamic friction, which ensures that genuine users have a smooth experience while preventing fraudsters from accessing services. Sift's business model is subscription-based, with fees that depend on transaction volume and service level. Additionally, Sift offers services like chargeback management and dispute resolution, which add further value to its offerings. The company's goal is to enhance digital trust and safety for businesses by providing tools that help them make informed decisions and protect against fraud.
Key Metrics
Bristol, United KingdomHeadquarters
2011Year Founded
$4.4MTotal Funding
SERIES_ACompany Stage
AI & Machine Learning, Financial ServicesIndustries
51-200Employees