Software Architect - Machine Learning
Beyond Sports22 hours ago
Budapest, HungaryStaff+
Responsibilities
- Shape architecture, technical direction, and implementation strategy for ML-driven products and platforms.
- Design scalable inference pipelines, motion-synthesis systems, real-time data processing solutions, and cloud-hosted ML services.
- Build proofs of concept, evaluate technical approaches, and review model deployment strategies.
- Guide engineers and squads through complex architecture, integration, and implementation challenges.
- Ensure solutions are scalable, reusable, maintainable, and aligned with engineering standards.
- Collaborate with Machine Learning Engineers, Tech Leads, Product teams, architects, artists, and other multidisciplinary stakeholders.
- Provide hands-on technical leadership through prototyping, code-based validation, and support for difficult engineering challenges.
Requirements
- Strong experience in machine-learning engineering, software architecture, or technical leadership.
- Deep expertise in ML systems, data pipelines, backend engineering, or cloud infrastructure.
- Experience deploying ML systems into production, including serving, monitoring, and retraining.
- Familiarity with MLOps tooling such as MLflow, CI/CD workflows, and model monitoring.
- Experience with AWS, Azure, or GCP.
- Experience with sequence models such as transformers or RNNs, or generative models such as diffusion, flow, or GANs.
- Hands-on experience building and training deep-learning models in PyTorch.
- Strong understanding of scalable APIs, cloud-native systems, and modern engineering practices.
- Experience creating proofs of concept and evaluating technical approaches.
- Ability to communicate technical solutions clearly to technical and non-technical audiences.
- Experience collaborating across multidisciplinary engineering teams.
- Understanding of real-time systems, motion synthesis, or 3D graphics pipelines is a preferred qualification.
- Experience with Unity or other game engines is a preferred qualification.
Benefits
- High-impact work shaping how millions of people experience sports globally.
- Technical ownership over implementation strategy, architecture, and engineering standards.
- Hands-on innovation through building proofs of concept and defining ML systems.
- Ongoing learning across machine-learning research and real-time 3D systems.
- Collaboration with engineers, artists, ML specialists, and product teams.
Tech Stack
Categories