Senior Machine learning Engineer

• Design and maintain robust GitHub Workflows and CI pipelines for new and existing feature teams.
• Develop KPI dashboards using Grafana to monitor compute usage, GPU efficiency, unit test durations, and overall system health.
• Manage dependency updates (Torch upgrades, Ubuntu updates, Hydra maintenance, Dependabots) to ensure a secure and modern stack.
• Drive software design excellence by performing thorough code reviews (PRs) and enforcing high standards in software architecture. Embedded & Evaluation:
• Establish scalable evaluation pipelines for embedded targets, specifically for QNN boards and other edge devices.
• Collaborate with feature teams to support model compression experiments and on-target performance verification.

• Education: Bachelor’s degree in Computer Science, Electrical Engineering, or a related field. An advanced degree is an advantage.
• Experience: 5+ years of industry experience in MLOps, Data Engineering, or Software Infrastructure, with a focus on Deep Learning systems.
• Programming & Software Design: Expert-level proficiency in Python with a strong emphasis on clean software design, object-oriented programming, and architectural patterns.
• Infrastructure & Orchestration: Deep hands-on experience with Kubernetes, Docker, and Cloud platforms (specifically Azure ML, Azure Storage/Networking).
• Big Data & Optimization: Proficiency with high-performance data processing tools such as Ray, PyArrow, and SQL.

optimizing data loading bottlenecks for GPU training.

• DevOps & Monitoring: Experience setting up complex CI/CD pipelines (GitHub Actions) and observability stacks (Grafana, Prometheus).
• Soft Skills: Strong problem-solving abilities, proactiveness, and ownership of complex topics. Ability to adapt quickly to new technologies and work collaboratively in a supportive, high-performance team.

Bachelor’s degree in Computer Science, Electrical Engineering, or a related field.

An advanced degree is an advantage.

• Deep Learning Knowledge: While deep algorithm knowledge is not mandatory, a strong understanding of Deep Learning workflows (PyTorch, training loops, multi-task learning) is highly beneficial.
• Embedded Systems: Experience with deploying or evaluating models on embedded hardware (Qualcomm QNN, etc.) or setting up hardware-in-the-loop pipelines.
• T-Shaped Skills: Ability to specialize deeply in infrastructure while lending a hand to feature teams on diverse topics (e.g., documentation builds, visualization tools).