Member of Technical Staff (MLE) (AI Systems & Model Infrastructure)

Location: New York, United States; relocation support considered.

Compensation: $1.5–$2M total compensation, including base, bonus, and RSUs.

Company

hirify.global is a specialist recruitment partner focused on AI, machine learning, data, and infrastructure talent, representing a highly resourced AI lab building machine learning systems for complex global markets and large-scale investment decisions.

What you will do

• Own machine learning models and systems from research concepts through production deployment.
• Partner with research scientists to test ideas, shape experiments, and translate methods into scalable systems.
• Build and improve model training, evaluation, diagnostics, and deployment pipelines.
• Work across classical machine learning, neural networks, tabular models, LLM-adjacent systems, and custom modeling approaches.
• Design production-quality software and infrastructure that improves experimentation speed, reliability, interpretability, and model diagnosability.
• Take ownership of ambiguous, complex, and open-ended technical problems in a research-heavy environment.

Requirements

• Strong software engineering fundamentals, ideally from high-performance engineering environments.
• Hands-on experience building, training, debugging, or deploying machine learning models.
• Strong Python skills and practical experience with ML and data tooling such as NumPy, pandas, PyTorch, scikit-learn, or similar.
• Ability to reason about model behavior, evaluation, diagnostics, bias/variance, and experimental design.
• Experience delivering zero-to-one projects rather than only modifying mature codebases or working within heavy internal scaffolding.
• Comfort collaborating with researchers, scientists, product-minded engineers, and technical stakeholders.
• Track record of strong trajectory, high ownership, and intellectual curiosity.
• Ideally 5–10+ years of relevant experience; exceptional earlier-career candidates with steep trajectories may also be considered.

Nice to have

• Experience with distributed compute, large-scale experimentation tooling, Ray, distributed ML systems, cloud infrastructure, large-scale AWS environments, production ML pipelines, and model diagnostics tooling.

Culture & Benefits

• Permanent role with direct ownership of models, systems, and outcomes.
• Work alongside machine learning engineers and research scientists on causal reasoning, model diagnosability, tabular prediction, LLM reasoning, and production ML systems.
• Combination of startup-like pace and ownership with the compute, data, resources, and stability of a major global institution.
• Models and systems have direct real-world impact on large-scale investment decisions.