Job Description
Are you ready to build the future of Generative AI? At Synthetix AI Labs, we are pushing the boundaries of what is possible with large language models (LLMs) and cognitive agents. We are looking for a visionary Senior AI/ML Engineer to join our elite, fast-paced team in San Francisco. In this role, you will lead the architecture, fine-tuning, and deployment of proprietary models that power next-generation workflows for millions of users worldwide.
This isn't just another engineering job; it's an opportunity to shape the core intelligence of a platform redefining human-machine collaboration. You will work alongside world-class researchers, have access to state-of-the-art compute infrastructure, and directly impact the strategic direction of our product roadmap.
Responsibilities
- Architect, fine-tune, and deploy highly performant, domain-specific Large Language Models (LLMs) to production.
- Optimize inference performance, throughput, and latency using state-of-the-art quantization and serving frameworks (e.g., vLLM, TensorRT-LLM).
- Design and implement robust Retrieval-Augmented Generation (RAG) pipelines leveraging advanced vector search and semantic routing.
- Collaborate with product and front-end engineering teams to turn raw ML capability into seamless, intuitive user experiences.
- Establish rigorous evaluation frameworks to continuously benchmark model performance, alignment, and safety metrics.
- Provide technical leadership and mentorship to junior team members while championing MLOps best practices.
Qualifications
- Master’s or Ph.D. in Computer Science, Artificial Intelligence, or a highly quantitative field.
- 4+ years of industry experience building and deploying deep learning models in high-scale production systems.
- Expertise in Python and core deep learning frameworks, specifically PyTorch.
- Proven track record of fine-tuning open-source LLMs (e.g., LLaMA, Mistral) using advanced PEFT techniques (QLoRA, LoRA).
- Deep hands-on experience with vector databases (Pinecone, Qdrant, Milvus) and LLM orchestration tools (LangChain, LlamaIndex).
- Strong understanding of software engineering fundamentals, containerization (Docker, Kubernetes), and cloud-native ML infrastructure.