Job Description
Are you ready to shape the landscape of Artificial Intelligence in 2026? Apex Dynamics is seeking a visionary Senior AI/ML Engineer to lead our next-generation generative AI initiatives. We are building the systems that will define the future of human-computer interaction and intelligent automation.
In this pivotal role, you will bridge the gap between cutting-edge research and scalable production systems. You will work with a world-class team to develop Large Language Models (LLMs), build robust Retrieval-Augmented Generation (RAG) pipelines, and deploy ethical AI solutions that drive measurable business impact.
Why Join Us?
- Work on projects that push the boundaries of what's possible with AI.
- Competitive compensation and equity packages.
- Flexible remote-first culture with a hub in San Francisco.
- Access to the latest hardware and research tools.
Responsibilities
- Model Development: Design, train, and fine-tune state-of-the-art machine learning models, with a focus on Transformers and Generative AI architectures.
- System Architecture: Architect scalable MLOps pipelines and cloud infrastructure (AWS/GCP) to handle high-throughput data processing and model serving.
- Performance Optimization: Implement techniques such as quantization, pruning, and distributed training to optimize model inference latency and cost.
- Data Engineering: Collaborate with data scientists to curate high-quality datasets and implement data validation and preprocessing workflows.
- Cross-Functional Collaboration: Partner with product managers and engineering teams to translate complex technical requirements into actionable features.
- Research & Innovation: Stay abreast of the latest academic research in NLP and Deep Learning to continuously improve our model capabilities.
- Ethical AI: Ensure AI systems are fair, transparent, and aligned with safety guidelines.
Qualifications
- Education: Master’s or Ph.D. in Computer Science, Mathematics, or a related field (or equivalent practical experience).
- Experience: 5+ years of professional experience in Machine Learning Engineering or Applied AI.
- Technical Skills: Proficiency in Python, PyTorch, TensorFlow, and C++. Strong understanding of deep learning frameworks and algorithms.
- LLM Expertise: Hands-on experience with Large Language Models, fine-tuning methodologies, and prompt engineering.
- Infrastructure: Experience with cloud platforms (AWS, GCP), Kubernetes, Docker, and vector databases (Pinecone, Milvus).
- Problem Solving: Proven track record of solving complex technical challenges in high-scale environments.
- Communication: Excellent verbal and written communication skills, capable of presenting technical concepts to non-technical stakeholders.