Job Description
We are building the infrastructure for the next evolution of artificial intelligence. As a leader in the 2026 AI revolution, we are seeking a visionary AI/ML Engineer to architect, train, and deploy large-scale generative models that redefine human-machine interaction.
In this role, you won't just be maintaining models; you will be defining the roadmap for autonomous systems, ethical AI integration, and next-generation NLP capabilities. Join a team of world-class researchers and engineers dedicated to pushing the boundaries of what is possible in the age of AGI.
Why Join Us?
- Work on the cutting edge of LLMs and Generative AI.
- Competitive salary and equity packages for top-tier talent.
- Flexible remote-first culture with a hub in San Francisco.
Ready to shape the future? Apply today.
Responsibilities
- Model Architecture: Design and implement scalable deep learning architectures for Large Language Models (LLMs) and multimodal systems targeting 2026 capabilities.
- Optimization: Engineer high-performance inference pipelines to reduce latency and cost while maximizing model accuracy.
- Data Strategy: Lead the creation of synthetic data pipelines and ensure rigorous data quality standards for training robust models.
- Research Integration: Stay ahead of the curve by integrating the latest research findings from top-tier conferences (NeurIPS, ICML) into production systems.
- Ethical AI: Implement guardrails and safety measures to ensure AI outputs align with ethical guidelines and regulatory standards.
- Collaboration: Partner with product and engineering teams to translate complex AI research into user-friendly, scalable applications.
Qualifications
- Education: MS or PhD in Computer Science, Machine Learning, or a related field from a top-tier institution.
- Experience: 5+ years of professional experience in AI/ML engineering, with a proven track record of deploying production-grade models.
- Technical Skills: Proficiency in Python, PyTorch, TensorFlow, and distributed computing frameworks (Ray, Kubernetes).
- Modeling: Deep expertise in Transformer architectures, BERT, GPT, and fine-tuning techniques.
- Problem Solving: Strong analytical skills with the ability to debug complex system issues and optimize performance bottlenecks.
- Communication: Excellent ability to communicate technical concepts to both technical and non-technical stakeholders.