ML engineer
Xforia Inc
What you will do:
- Optimize trained models for inference, ensuring they are ready for deployment.
- Work closely with the internal AI research team to integrate inference optimization into the training process.
- Provide expertise to help customers deploy customized inference services on our cloud.
- Design and develop tools and systems that enable ML cycles, such as CI/CD/CT pipelines, with an emphasis on inference.
- Deploy code/solutions to different environments, including batch and streaming/microservices architectures.
- Serve as domain experts in ML inference, working closely with both internal teams and external customers.
- Ensure models are efficiently deployed across various platforms, including cloud and standalone environments.
- Collaborate closely with MLOps team members to design and implement scalable, efficient ML systems.
- Continuously improve deployment processes and infrastructure to enhance model performance and reliability.
- Improve the reliability, security, scalability, and observability of our distributed inference infrastructure.
- Build tools to give visibility into bottlenecks and sources of instability and design and implement solutions to address high-priority issues.
- Support benchmarking across different GPU accelerators and capture benchmarking data by writing tools and capturing data.
- Strong Python skills and experience in model deployment.
- Experience with ML systems, particularly in deploying and optimizing modern LLMs.
- Familiarity with transformer architectures and fine-tuning models for inference.
- Ability to build and deploy ML models in cloud environments (e.g., AWS, Azure, GCP) and standalone systems.
- Experience with batch processing and streaming/microservices architectures.
- Proficiency in testing, debugging, and maintaining systems.
- Familiarity with MLPerf Benchmarks and Language Model Evaluation Harness is a big plus.
- Ability to collaborate effectively with cross-functional teams and provide technical guidance.
- Excellent problem-solving skills and a proactive attitude towards challenges.
- Own problems end-to-end and willingness to pick up new knowledge as needed to get the job done.
- Work experience at a cloud provider or AI compute/sub-system company.
- Experience with deep learning frameworks (such as PyTorch, TensorFlow).
- Experience with deep learning runtimes (such as ONNX Runtime, TensorRT).
- Experience with inference servers/model serving frameworks (such as Triton, TFServ, CubeFlow).
- Experience with distributed systems collectives such as NCCL, OpenMPI.
- Experience deploying ML workloads on distributed systems.
- Understanding of the nuances in training and deployment of distributed ML models, and familiarity with techniques like quantization, sparsity, etc.
- Good intuition for when off-the-shelf solutions will work and the ability to build tools quickly if they won't.
- A humble attitude, eagerness to help colleagues, and a commitment to team success.
- Opportunity to work with cutting-edge technology and make a significant impact on ML inference capabilities.
- Collaborative and inclusive work environment.
- Competitive compensation and benefits package.
- Access to state-of-the-art research facilities and resources.
- Encouragement and support for continuous learning and professional development.
Vacancy posted 6 hours ago
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