Member of Technical Staff, ML Product Engineering
Bay Area
- Pay
- Salary not listed in the saved posting
- Work setup
- Unconfirmed
- Employment
- Unconfirmed
What you’ll work on
Full postingYou'll build our core product offerings, partner with customers, and ensure our models perform reliably at scale in production environments.
Design, develop, and optimize our models for production use cases.
Partner with customers to understand their requirements and translate them into technical solutions.
From the employer’s posting
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
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.
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.
What you’ll bring
All qualificationsCore experience
- 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 experience
- 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).
Qualification wording
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.
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).
Tools in this posting
- AWS
- Google Cloud (GCP)
- PyTorch
- Azure
- Docker
Source — Tool mentions in context
- Experience with LLM serving frameworks like vLLM, SGLang, or TensorRT. - Experience with cloud platforms (AWS, GCP, Azure). - Experience with model quantization and optimization techniques.
- 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).
- 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.
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.
Your next step
- Have your CV and examples of relevant work ready.
- Check the listed location, eligibility and core experience before starting.
- Ask the employer about the salary range before committing time to the process.
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Source & posting history
Source notes
Source excerptsSelected passages from the saved posting. Check the full description for conditions and exceptions.
- Pay
No pay amount identified in the saved description.
- Location & working pattern
Bay Area
Working pattern and location restrictions need checking in the full posting.
- Work authorization
No clear work-authorization passage found. Eligibility is unconfirmed.
- Status in our records
- Active
- First seen by us
- Jun 2, 2026
- Recorded sightings
- 64
- Last seen by us
- Oct 2, 2026
These dates show when we found the listing. Check the employer’s website to confirm it is still accepting applications.
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