Job Description
Are you ready to shape the future of generative intelligence? Synthetix AI Labs is seeking a world-class Senior Machine Learning Engineer to join our core AI Platform team in San Francisco. In this role, you will bridge the gap between cutting-edge research and production-grade software, designing agentic workflows and fine-tuning state-of-the-art foundation models that power next-generation enterprise solutions.
We are a fast-growing, venture-backed startup composed of ex-FAANG researchers and industry veterans. We offer a high-autonomy, high-impact environment, competitive compensation, and direct access to state-of-the-art H100 compute clusters.
Responsibilities
- Design, build, and deploy scalable LLM orchestration pipelines using LangChain, LlamaIndex, and custom agent frameworks.
- Fine-tune open-source foundation models (such as LLaMA, Mistral, and Mixtral) for highly specialized downstream tasks and domain-specific applications.
- Optimize model inference latency, throughput, and memory footprint utilizing TensorRT-LLM, vLLM, and advanced quantization techniques.
- Collaborate closely with product and core software engineering teams to seamlessly integrate AI agents into consumer-facing applications.
- Establish robust MLOps, CI/CD, and evaluation frameworks to monitor model drift, bias, and performance in real-time.
- Mentor junior machine learning engineers and champion software engineering best practices within the AI organization.
Qualifications
- Master’s or Ph.D. in Computer Science, Mathematics, or a highly quantitative field with a deep learning focus.
- 4+ years of professional software engineering experience, with at least 2 years deploying production-grade ML systems at scale.
- Deep, hands-on expertise with PyTorch, the Hugging Face ecosystem, and modern transformer-based architectures.
- Proven experience working with vector databases (e.g., Pinecone, Milvus, Qdrant) and implementing complex Retrieval-Augmented Generation (RAG) pipelines.
- Advanced proficiency in Python and solid experience with containerization (Docker, Kubernetes) and cloud platforms (AWS or GCP).
- Strong track record of optimizing distributed training runs and building high-performance APIs.