Senior Software Engineer - AI Data
New
Germany / Ireland, United KingdomFull-TimeSenior
Salary141267 - 184512 EUR per year
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Job Details
- Experience
- 5+ years
- Required Skills
- DockerPythonSQLGCPAirflowRESTful APIsBigQueryDatadogDistributed SystemsPySpark
Requirements
- 5+ years of professional software engineering experience
- Strong proficiency in Python
- Strong proficiency in SQL
- Demonstrated ability to write production-quality code
- Solid understanding of software engineering fundamentals: Data structures and algorithms
- Solid understanding of software engineering fundamentals: System design and architectural patterns
- Solid understanding of software engineering fundamentals: Testing strategies (unit, integration, end-to-end)
- Solid understanding of software engineering fundamentals: Code review practices and technical collaboration
- Experience with RESTful APIs and distributed systems concepts
- Experience with Containerization (Docker) and basic cloud infrastructure
- Track record of delivering high-quality software in a team environment
- Ability to thrive in a startup environment with changing priorities and rapid iteration
- Experience with GCP services (BigQuery, GCS, Cloud Run, GKE) (Preferred)
- Familiarity with distributed processing frameworks (Apache Beam, PySpark) (Preferred)
- Experience with workflow orchestration tools (Airflow, Prefect, Dagster) (Preferred)
- Understanding of ML/AI infrastructure and data pipelines (Preferred)
- Experience with monitoring and observability tools (Datadog) (Preferred)
- Experience working with researchers directly (Preferred)
- Background in data engineering roles (Preferred)
Responsibilities
- Architect Next-Gen AI Data Infrastructure
- Design scalable, future-proof data platforms optimized for AI research workloads
- Build efficient self-serve data processing pipelines leveraging GCP's advanced services
- Implement cost-effective storage and monitoring solutions for ML at scale
- Create flexible training resource management with intelligent queuing
- Optimize resource allocation for maximum training efficiency
- Participate in on-call rotation to ensure system reliability
- Lead adoption of cutting-edge ML tools and frameworks, continuously evaluating and integrating best-in-class solutions
- Streamline existing workflows while introducing new tooling that further reduces complexity
- Enhance our tooling and documentation to accelerate team velocity and maintain our competitive edge
- Implement guardrails for cost, quality, and performance
- Identify and eliminate technical bottlenecks in the data processing and training pipelines
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