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Member of Technical Staff, ML Product Engineering
San Mateo, USAPosted 3 months ago
Full-timeremote
Job Description
The Role
We seek experienced engineers and scientists to bridge the gap between research and real-world applications by training and deploying our diffusion large language models. You'll build our core product offerings, partner with customers, and ensure our models perform reliably at scale in production environments.
Key Responsibilities
- Design, develop, and optimize our models for production use cases.
- Partner with customers to understand their requirements and translate them into technical solutions.
- Implement innovative approaches for post-training generative AI models, including agentic workflows.
- Work on data preprocessing pipelines, model evaluation, and alignment to enterprise use cases.
- Contribute to the deployment and maintenance of models in production environments.
- Collaborate with product teams to design and implement customer-facing ML features.
Qualifications
- BS/MS/PhD in Computer Science, Machine Learning, or a related field (or equivalent experience).
- At least 5 years of experience working on ML projects in PyTorch (or equivalent), preferably in a research lab or engineering role.
- Excellent familiarity with transformers and core LLM concepts (autoregressive pretraining, instruction tuning, in-context learning, LoRA, KV caching).
- Experience training LLMs, including fine-tuning.
- Familiarity with large-scale systems and high-performance computing, including GPU/TPU utilization.
- Experience with version control (Git) and containerization (Docker).
- Excellent communication skills with the ability to explain technical concepts to non-technical stakeholders.
Preferred Skills
- Expertise in data engineering and synthetic data generation for LLMs.
- Knowledge of MLOps and production-level deployment workflows.
- Experience with LLM serving frameworks like vLLM, SGLang, or TensorRT.
- Experience with cloud platforms (AWS, GCP, Azure).
- Experience with model quantization and optimization techniques.