Job Description
We are seeking a visionary Senior AI/ML Engineer to join our elite engineering team in San Francisco. As we pioneer the technological landscape for the 2026 era, we are building next-generation Large Language Models (LLMs) and autonomous agent frameworks that will redefine human-computer interaction.
About the Role:
In this high-impact position, you will not just write code; you will architect the future. You will work at the intersection of cutting-edge research and scalable production engineering. If you are passionate about pushing the boundaries of what's possible in artificial intelligence and want to lead a team that is shaping the digital ecosystem of tomorrow, we want to hear from you.
Why Join Us?
- Work with state-of-the-art technology stacks.
- Competitive equity package and benefits.
- Flexible remote-first culture with a San Francisco hub.
Responsibilities
- Design, train, and deploy production-ready Machine Learning models focusing on Natural Language Processing (NLP) and Computer Vision.
- Lead the architecture of scalable data pipelines to support model training and inference at scale.
- Collaborate with cross-functional teams of researchers and product managers to translate business requirements into technical solutions.
- Optimize existing models for latency, throughput, and cost efficiency using techniques like quantization and distillation.
- Mentor junior engineers and conduct code reviews to ensure high standards of software engineering and AI best practices.
- Stay abreast of the latest advancements in the AI field and integrate relevant innovations into our product roadmap.
Qualifications
- PhD or Master’s degree in Computer Science, Mathematics, Statistics, or a related field.
- Minimum of 5+ years of professional experience in AI/ML engineering.
- Expert proficiency in Python and deep familiarity with ML frameworks such as PyTorch or TensorFlow.
- Strong understanding of deep learning architectures, transformer models, and training methodologies.
- Experience with MLOps tools (e.g., MLflow, Kubeflow) and cloud platforms (AWS, GCP, or Azure).
- Proven track record of shipping models into production environments.