Junior SRE

1 мес. назад

United StatesUnited KingdomWorldwideJunior

machine learningdata pipelinesworkflow orchestrationinfrastructureperformance optimizationscalability

Junior Software Engineer responsible for contributing to production ML/HPC infrastructure and systems with mentorship and increasing autonomy.

Требования

A degree (BSc or MSc) in Computer Science, Engineering, Applied Mathematics, or a related field, or equivalent practical experience.
Strong foundations in computer science (e.g. algorithms, data structures, operating systems, distributed systems).
Experience developing software in at least one language such as Python, C++, or Rust.
Familiarity with writing, testing, and maintaining production‑quality code.
Ability to reason clearly about technical problems and communicate effectively with teammates.
Interest in infrastructure, performance, and large‑scale systems.
Exposure to machine learning workflows, data engineering, or ML platforms.
Experience working in Linux environments.
Introductory knowledge of distributed systems, HPC, or cloud platforms for CPU and GPU workloads.
Experience with profiling, benchmarking, or performance analysis.

Будет плюсом

Familiarity with CUDA or other GPU development frameworks.

Другое

 
This is a Full‑time Junior Software Engineering position for candidates early in their career who are interested in infrastructure, systems, and machine learning platforms at scale.
As a Junior Engineer, you will contribute to production ML/HPC infrastructure, working alongside experienced engineers and quantitative researchers. You will gradually take ownership of components and features, learn how large‑scale systems are designed and operated in practice, and contribute production‑quality code that directly supports research and trading workloads.
This role is designed to offer strong mentorship while encouraging increasing autonomy and technical ownership over time.
As part of the team, you will work on some of the following:
Design, implement, and maintain data pipelines for machine learning training and inference.
Develop and improve workflow orchestration and infrastructure components (e.g.  scheduling, resource allocation, data movement, filesystems) .
Analyze and improve performance and scalability of systems and critical code paths.
Improve the reliability, observability, and usability of ML or HPC platforms.
Collaborate with quantitative researchers and developers to understand requirements and translate them into robust technical solutions.
The opportunity to work on production‑grade ML and HPC infrastructure at scale.
Strong mentorship and technical guidance from experienced engineers.
Gradual ownership of meaningful systems and components.
Exposure to real‑world performance, scalability, and reliability challenges.
Close collaboration with quantitative researchers and developers.
You will be expected to learn existing systems, contribute independently to ongoing work, and progressively take ownership of well‑defined areas of the platform with support from more senior engineers.