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Tech Stack
Tools & technologiesAirflowCloudDistributed SystemsKubernetes
About the role
Key responsibilities & impact- Design and implement enterprise-scale ML infrastructure
- Architect high-performance model serving solutions handling millions of predictions per second
- Build robust, scalable systems for model training, serving and inferencing
- Lead technical decisions for critical ML platform components
- Develop automated ML pipelines using “State of art” tools
- Implement sophisticated monitoring and observability solutions
- Optimize resource utilization across large scale distributed computing environments
- Design fault-tolerant, highly available ML systems using Airflow or MLflow, HPC (SLURM)/ GPU
Requirements
What you’ll need- 10+ years of software engineering experience with distributed systems
- 4+ years of hands-on experience building ML platforms
- Proven experience with Kubernetes, containerization, and cloud platforms
- Strong background in performance optimization and scalability
- Experience with Jupyter Hub, MLflow, or similar ML platforms
Benefits
Comp & perks- Market leader in compensation and equity awards
- Comprehensive physical and mental wellness programs
- Competitive vacation and holidays for recharge
- Paid parental and adoption leaves
- Professional development opportunities for all employees regardless of level or role
- Employee Networks, geographic neighborhood groups, and volunteer opportunities to build connections
- Vibrant office culture with world class amenities
- Great Place to Work Certified™ across the globe
ATS Keywords
✓ Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills & Tools
machine learningdistributed systemsperformance optimizationscalabilityautomated ML pipelinesmodel trainingmodel servingmodel inferencingmonitoring solutionsobservability solutions
Soft Skills
leadershiptechnical decision-making