Required Machine Learning Platform Engineer
Team Overview
The ML Platform team builds the core infrastructure, services, and systems that enable machine learning at scale across the company.
We own the full lifecycle of ML workloads – from research and experimentation to reliable, large-scale production deployment.
The team focuses on:
Building scalable backend and infrastructure solutions for end-to-end ML workflows
Developing core ML platform components, including data processing, training, evaluation, and deployment
Empowering data scientists and product teams with robust, observable, and high-performance ML infrastructure
Solving complex challenges around scale, latency, throughput, reliability, and cost efficiency
Continuously evolving the ML platform by adopting new technologies and improving system performance and stability
What will you do?
As a ML Platform Engineer, you will design and build core backend systems and ML infrastructure that power research, training, and online inference at scale.
You will work at the intersection of backend engineering, data engineering, and machine learning, partnering closely with data scientists to transform research needs into production-grade platform capabilities with real business impact.
Key Responsibilities
Design, build, and own backend systems that support ML workflows, data processing, and core platform services
Architect and optimize systems for performance, scalability, reliability, and cost efficiency in production ML environments
Collaborate closely with data scientists to implement, iterate, and evolve ML platform capabilities
Build and maintain large-scale data ingestion, processing, and training pipelines
Ensure systems are production-ready through testing, observability, monitoring, and performance optimization
Investigate and resolve infrastructure and performance issues affecting critical ML and business KPIs.
Team Overview
The ML Platform team builds the core infrastructure, services, and systems that enable machine learning at scale across the company.
We own the full lifecycle of ML workloads – from research and experimentation to reliable, large-scale production deployment.
The team focuses on:
Building scalable backend and infrastructure solutions for end-to-end ML workflows
Developing core ML platform components, including data processing, training, evaluation, and deployment
Empowering data scientists and product teams with robust, observable, and high-performance ML infrastructure
Solving complex challenges around scale, latency, throughput, reliability, and cost efficiency
Continuously evolving the ML platform by adopting new technologies and improving system performance and stability
What will you do?
As a ML Platform Engineer, you will design and build core backend systems and ML infrastructure that power research, training, and online inference at scale.
You will work at the intersection of backend engineering, data engineering, and machine learning, partnering closely with data scientists to transform research needs into production-grade platform capabilities with real business impact.
Key Responsibilities
Design, build, and own backend systems that support ML workflows, data processing, and core platform services
Architect and optimize systems for performance, scalability, reliability, and cost efficiency in production ML environments
Collaborate closely with data scientists to implement, iterate, and evolve ML platform capabilities
Build and maintain large-scale data ingestion, processing, and training pipelines
Ensure systems are production-ready through testing, observability, monitoring, and performance optimization
Investigate and resolve infrastructure and performance issues affecting critical ML and business KPIs.
Requirements:
3+ years of experience in backend and/or ML infrastructure engineering
Strong foundation in system design, APIs, distributed systems, and performance optimization
Hands-on experience with ML frameworks such as TensorFlow or PyTorch
Advanced backend development skills in Python
Experience working with cloud infrastructure (AWS or GCP), including services such as AWS Batch, S3, and GPU-based workloads
Strong communication skills and ability to collaborate across engineering, data science, and product teams
Masters degree in Computer Science, Engineering, or a related technical field is an advantage
Bonus / Plus Points
Backend experience in Java, Scala, Go, or Rust
Experience with large-scale data systems and tools such as Spark, Airflow, or BigQuery
Performance engineering expertise, including profiling, benchmarking, and tuning ML workloads
Hands-on experience building or maintaining production ML pipelines and flows
Please submit your CV in English.
3+ years of experience in backend and/or ML infrastructure engineering
Strong foundation in system design, APIs, distributed systems, and performance optimization
Hands-on experience with ML frameworks such as TensorFlow or PyTorch
Advanced backend development skills in Python
Experience working with cloud infrastructure (AWS or GCP), including services such as AWS Batch, S3, and GPU-based workloads
Strong communication skills and ability to collaborate across engineering, data science, and product teams
Masters degree in Computer Science, Engineering, or a related technical field is an advantage
Bonus / Plus Points
Backend experience in Java, Scala, Go, or Rust
Experience with large-scale data systems and tools such as Spark, Airflow, or BigQuery
Performance engineering expertise, including profiling, benchmarking, and tuning ML workloads
Hands-on experience building or maintaining production ML pipelines and flows
Please submit your CV in English.
This position is open to all candidates.
