Senior Software Engineer, Machine Learning Infrastructure
Tasks
- Build and operate production ML and AI infrastructure
- Build evaluation infrastructure for LLMs
- Create benchmarks and quality measurement pipelines
- Develop and scale LLM platform
- Develop fine tuning and reinforcement learning pipelines
- Enable batching and autoscaling
- Establish ML infrastructure best practices
- Implement GPU serving for inference
- Implement observability for LLM systems
- Improve reliability and scalability of ML platform
- Integrate LLM providers and manage orchestration
- Optimize inference infrastructure for open and fine tuned models
- Partner with AI Data Science and Product teams to productionize models
- Support post training workflows
Perks/Benefits
- 401k match
- Equity
- Fertility benefits
- Flexible PTO
- Learning stipend
- Medical/Dental/Vision
- Mental health support
- Paid parental leave
- Remote work support
- Wellness stipend
Skills/Tech-stack
AWS | Airflow | Apache Beam | Apache Spark | Autoscaling | Benchmarking | BigQuery | Bigtable | CI/CD | Dataflow | Docker | Embeddings | Feature Engineering | Feature Stores | Fine Tuning | GCP | GPU Inference | GPU serving | Go | Inference Batching | Kubernetes | LLM orchestration | ML observability | Machine Learning | Model Serving | Observability | Performance optimization | Provider APIs | PyTorch | Python | Quality Regression Testing | RLHF | Ray | Redis | Regression testing | Reinforcement Learning | Reward Modeling | Streaming Data | Terraform | Training pipelines | Triton | TypeScript | VLLM | Vertex AI
Education
N/A
Regions
Countries
States
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