About the Opportunity
We are partnering with one of the most advanced research-driven technology organizations in the world, operating at the intersection of machine learning, large-scale systems, and real-world decision making.
This organization applies state-of-the-art machine learning, deep learning, and statistical modeling to some of the most complex data and systems challenges globally.
Their research teams operate much like elite applied research labs, combining mathematical rigor, production engineering, and frontier ML development to build systems with measurable real-world impact.
They are expanding multiple ML Researcher / ML Engineer teams focused on LLMs and next-generation model architectures.
This is an opportunity to work alongside world-class researchers and engineers on problems that demand both theoretical depth and practical implementation at scale.
Role Focus
There are two primary tracks within the team:
1️⃣ Model Architecture & Pre-Training Research
- Design and build large-scale models from first principles
- Experiment with architecture design and scaling behavior
- Explore attention variants, normalization strategies, routing mechanisms, and training stability
- Run rigorous ablation experiments and performance analysis
- Work on training infrastructure and model scaling
2️⃣ Post-Training & LLM Optimization
- Fine-tune and align large language models for complex applications
- Develop advanced post-training techniques and evaluation methods
- Integrate LLM systems into large-scale decision platforms
- Optimize models for performance, efficiency, and reliability
What You'll Work On
- Designing and training advanced machine learning models
- Developing research pipelines in Python / PyTorch
- Running controlled experiments and analyzing model performance
- Applying statistical and probabilistic reasoning to large datasets
- Collaborating with researchers and engineers to translate ideas into production systems
- Solving challenging problems involving large-scale compute, model optimization, and real-world data
Ideal Background
- 3–8 years of experience in machine learning research or advanced ML engineering
- Experience building, training, or deploying large-scale ML / LLM models
- Strong coding ability (Python required; C++ beneficial)
- Deep understanding of machine learning fundamentals and model architecture
- Research publications, open-source contributions, or conference work highly valued
- Strong academic background (Master’s required; PhD strongly preferred) from a top-tier university with outstanding academic performance (typically ~3.6+ GPA or equivalent)
Particularly Relevant Experience
Candidates from environments such as:
- Frontier AI labs
- Advanced research groups
- Quantitative research firms
- High-performance ML infrastructure teams
- Deep learning research organizations
Additional Details
- Location: Miami (primary research hub)
- Exceptional candidates may be considered in New York
- Onsite collaboration environment
- Highly competitive compensation structure
If you're interested in working on cutting-edge machine learning research with real-world impact, apply directly or reach out for a confidential conversation.