Principal Ml Engineer

7 days ago


Jakarta, Indonesia Neurons Lab Full time

**About the project**:
We're looking for an experienced ML Infrastructure Engineer who has successfully implemented large-scale ML infrastructure optimization projects. The primary focus is migrating and optimizing computer vision models from Nvidia GPU-based infrastructure to AWS Inferentia/Trainium while getting performance boost and cost reduction.

Current Infrastructure:

- ML Models: RetinaFace, OpenPose, CLIP, and other CV models
- Hardware: A10/T4 GPUs on EKS
- Serving: Triton Inference Server
- Orchestration: Mix of Kubernetes and Ray

**Stage**: Presale and Delivery

**Duration: 2 months (preliminary)**

**Capacity: part-time (20h/week)**

**Areas of Responsibility**:

- ** Technical Leadership**:

- Lead the architecture design for ML infrastructure modernization
- Define compilation and optimization strategies for model migration
- Establish performance benchmarking framework
- Set up monitoring and alerting for the new infrastructure
- ** Performance Optimization**:

- Implement efficient model compilation pipelines for Inferentia2
- Optimize batch processing and memory layouts
- Fine-tune model serving configurations
- Ensure latency requirements are met across all services
- ** Cost Optimization**:

- Analyze and optimize infrastructure costs
- Implement efficient resource allocation strategies
- Set up cost monitoring and reporting
- Achieve target cost reduction while maintaining performance

**Skills**:

- Proven track record of ML infrastructure optimization projects
- Hands-on experience with AWS Neuron SDK and Inferentia/Trainium deployment
- Deep expertise in PyTorch model optimization and compilation
- Experience with high-throughput computer vision model serving
- Production experience with both Kubernetes and Ray for ML workloads

**Knowledge**:

- ** Model Optimization Expertise**:

- Deep understanding of ML model architecture optimization
- Experience with model compilation techniques for specialized hardware (Inferentia/Trainium)
- Proficiency in optimizing computer vision models (CNN architectures)
- Knowledge of model serving optimization patterns
- ** Performance Optimization**:

- Advanced understanding of ML model inference optimization
- Expertise in batch processing strategies
- Memory layout optimization for vision models
- Experience with pipeline parallelism implementation
- Proficiency in latency/throughput optimization techniques
- ** Hardware Acceleration**:

- Deep knowledge of ML accelerator architectures
- Understanding of hardware-specific optimizations
- Experience with model compilation for specialized chips
- Proficiency in memory access pattern optimization
- ** Performance Analysis**:

- Proficiency in ML model profiling tools
- Experience with performance bottleneck identification
- Knowledge of performance monitoring techniques
- Ability to analyze and optimize inference patterns

**Nice to Have**:

- Experience with Ray architecture for ML serving
- Knowledge of distributed ML systems
- Understanding of ML pipeline optimization
- Experience with model quantization techniques

**Experience**:

- ** Model Optimization** (4+ years):

- Proven track record of optimizing large-scale ML inference systems
- Successfully implemented hardware-specific model optimizations
- Demonstrated experience with computer vision model optimization
- Led projects achieving significant performance improvements
- ** Proven Results** (Examples):

- Successfully optimized computer vision models similar to RetinaFace/CLIP
- Achieved significant cost reduction while maintaining performance
- Implemented efficient batch processing strategies
- Developed performance monitoring and optimization frameworks


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