Job Description
We are building the infrastructure for the autonomous intelligence era. Nexus Horizon AI is seeking a visionary Senior AI & Machine Learning Engineer to lead the development of next-generation generative models and neuromorphic computing systems. If you are passionate about pushing the boundaries of Large Language Models (LLMs), reinforcement learning, and scalable AI infrastructure, this is your opportunity to define the future.
Why Join Us?
- Impact: Work on projects that will redefine human-computer interaction.
- Autonomy: Lead architectural decisions in a fully remote-first, high-performance culture.
- Equity: Competitive equity package reflecting our high-growth trajectory.
Role Overview:
As a Senior AI Engineer, you will bridge the gap between theoretical research and production-grade deployment. You will work closely with our research science team to implement novel algorithms that power our flagship products.
Responsibilities
- Architect and train state-of-the-art deep learning models, specifically focusing on Transformers and diffusion models for 2026-era generative capabilities.
- Optimize model inference pipelines for low-latency, high-throughput environments using edge computing and distributed systems.
- Collaborate with cross-functional teams (Product, Data Engineering, Design) to translate complex AI capabilities into user-centric features.
- Conduct rigorous experimentation and A/B testing to validate model performance and user engagement metrics.
- Mentor junior engineers and data scientists, fostering a culture of continuous learning and technical excellence.
- Ensure data privacy, security, and ethical AI practices across all model deployments.
- Stay ahead of industry trends in AI safety, alignment, and multimodal learning.
Qualifications
- PhD or Master’s degree in Computer Science, Mathematics, or a related field with a focus on AI/ML.
- Minimum of 5+ years of professional experience in machine learning engineering and deep learning.
- Expert proficiency in Python, PyTorch, or TensorFlow, with proven experience in building production ML systems.
- Deep understanding of NLP, LLM fine-tuning (PEFT, LoRA), and RAG architectures.
- Strong background in distributed computing (Kubernetes, Docker, AWS/GCP) and cloud infrastructure.
- Experience with MLOps tools (MLflow, Kubeflow) and model versioning strategies.
- Excellent communication skills with the ability to articulate complex technical concepts to non-technical stakeholders.