Job Description
Join the Future with 2026
We are at the forefront of the next industrial revolution. At 2026, we aren't just predicting the future; we are building it. We are seeking a visionary Lead Machine Learning Engineer to spearhead the development of our proprietary Generative AI infrastructure. You will work in a high-performance, elite engineering environment, pushing the boundaries of what is possible with Large Language Models and autonomous agents.
Why join us?
- Impact: Your code will directly power the next generation of enterprise intelligence solutions.
- Culture: A meritocratic, innovative culture that values deep technical excellence and creative problem-solving.
- Perks: Top-tier compensation, comprehensive health coverage, equity, and remote flexibility.
If you are ready to leave your mark on the world of AI, we want to hear from you.
Responsibilities
- Architect and deploy scalable, high-performance machine learning pipelines and infrastructure.
- Lead the research and implementation of cutting-edge Large Language Model (LLM) fine-tuning and retrieval-augmented generation (RAG) strategies.
- Mentor and guide a team of junior engineers and data scientists, fostering a culture of continuous learning and technical rigor.
- Collaborate closely with product and research teams to translate complex business requirements into robust technical solutions.
- Optimize model inference speed and reduce latency for real-time applications.
- Stay ahead of the curve in AI research, evaluating and integrating new methodologies and frameworks.
Qualifications
- Bachelor’s or Master’s degree in Computer Science, Mathematics, or a related technical field; Ph.D. preferred.
- 5+ years of professional experience in Machine Learning Engineering, with at least 2 years in a lead or senior technical role.
- Deep expertise in Python, PyTorch, or TensorFlow, and experience with distributed training frameworks (Ray, Kubernetes, Spark).
- Strong background in NLP, specifically working with Transformers, BERT, GPT architectures, and LLM fine-tuning (LoRA, QLoRA).
- Experience with MLOps tools (MLflow, DVC, Seldon, Kubeflow) and cloud platforms (AWS, GCP, or Azure).
- Proven track record of shipping production-grade machine learning systems that impact business metrics.
- Excellent communication skills and the ability to articulate complex technical concepts to non-technical stakeholders.