Job Description
About Chronos Dynamics: We are a premier R&D firm pioneering the technology required for the 2026 horizon. We are not just building software; we are architecting the future of human-machine interaction. We are looking for a visionary Principal Machine Learning Engineer to lead our core research division.
The Role: You will be at the forefront of the Artificial General Intelligence (AGI) transition. You will define the architectural standards for our next-generation neural networks, ensuring scalability, efficiency, and ethical alignment. This is a high-impact role for an engineer who wants to leave a legacy in the tech landscape of 2026 and beyond.
Why Join Us?
- Work on cutting-edge Generative AI models.
- Competitive equity package and top-tier compensation.
- Flexible remote-first policy with a hub in San Francisco.
- Collaborate with world-class PhDs and engineers.
Responsibilities
- Design and deploy scalable distributed ML training pipelines for next-generation Large Language Models (LLMs).
- Lead the technical roadmap for the 2026 AI infrastructure, focusing on inference optimization and low-latency deployment.
- Mentor senior engineers and researchers, fostering a culture of innovation and technical excellence.
- Collaborate with product teams to translate complex research into deployable, production-grade solutions.
- Establish best practices for data governance, model monitoring, and explainability in AI systems.
- Research and prototype novel architectures capable of handling multimodal data inputs (text, vision, audio).
Qualifications
- Ph.D. or Master’s degree in Computer Science, Mathematics, or a related quantitative field.
- 8+ years of professional experience in machine learning engineering, with at least 3 years in a lead or principal role.
- Deep expertise in PyTorch, TensorFlow, or JAX, with proven experience in training large-scale models.
- Strong understanding of distributed systems, cloud infrastructure (AWS/GCP), and containerization (Docker/Kubernetes).
- Experience with model compression, quantization, and edge deployment strategies.
- Exceptional problem-solving skills and a track record of delivering high-impact projects under tight deadlines.
- Familiarity with AI safety and alignment research is a significant plus.