Machine Learning Engineer, AI Systems & Model Infrastructure
Compensation: [$1.5 - $2M]
Location: New York with relocation support considered
Company: A highly resourced AI lab
A specialist AI lab is building next-generation machine learning systems to understand complex global markets, reason over structured and unstructured data, and deploy models that directly influence large-scale investment decisions.
This is not a support function or internal AI tooling team. The group operates with direct ownership of its own models, systems, and outcomes, combining the pace and ownership of a startup with the backing, compute, data, and stability of a major global institution.
You’ll join a small, highly technical team of machine learning engineers and research scientists working side-by-side on hard AI problems: causal reasoning, model diagnosability, tabular prediction, LLM reasoning, large-scale experimentation, and production-grade ML systems.
Role Summary
We’re looking for a hands-on Machine Learning Engineer who can own the full model lifecycle: reading papers, prototyping ideas, collaborating deeply with scientists, scaling experiments, writing production-quality code, and deploying models into live systems.
This role is ideal for someone who sits at the intersection of strong software engineering and applied machine learning. You should be comfortable moving from zero-to-one exploration through to robust production implementation, and excited by a collaborative, intellectually rigorous environment where engineers are expected to shape the science — not simply implement it.
What You’ll Do
- Own ML models and systems from research concept through production deployment.
- Partner closely with research scientists to test ideas, shape experiments, and translate promising methods into scalable systems.
- Build and improve model training, evaluation, diagnostics, and deployment pipelines.
- Work across classical ML, neural networks, tabular models, LLM-adjacent systems, and custom modelling approaches.
- Design production-quality software in a fast-moving, research-heavy environment.
- Develop tooling and infrastructure that improves experimentation speed, model reliability, and interpretability.
- Help ensure models are diagnosable, explainable, and robust in dynamic real-world conditions.
- Operate with high ownership across ambiguous, complex, and open-ended technical problems.
What You’ll Bring
- Strong software engineering fundamentals, ideally developed in high-performance engineering environments.
- Hands-on experience building, training, debugging, or deploying machine learning models.
- Strong Python skills and practical experience with ML/data tooling such as NumPy, pandas, PyTorch, scikit-learn, or similar.
- Ability to reason deeply about model behavior, evaluation, diagnostics, bias/variance, and experimental design.
- Experience working on zero-to-one projects, not just modifying mature codebases or operating within heavy internal scaffolding.
- Comfort collaborating closely with researchers, scientists, product-minded engineers, and technical stakeholders.
- A track record of strong trajectory, high ownership, and intellectual curiosity.
- Ideally 5–10+ years of relevant experience, though exceptional earlier-career candidates with steep trajectory will be considered.
Tech Stack
- Python
- PyTorch
- NumPy / pandas
- scikit-learn
- Classical ML and neural network frameworks
- Distributed compute and large-scale experimentation tooling
- Cloud infrastructure, including large-scale AWS environments
- Ray / distributed ML systems
- Production ML pipelines and model diagnostics tooling
Why Join?
This is a rare opportunity to work on genuinely hard AI problems with direct real-world impact.
You’ll have the chance to own models end-to-end, collaborate with exceptional research scientists, and work on systems where engineering quality is a strategic advantage. The team offers the intellectual depth of an AI research lab, the urgency and ownership of an early-stage company, and the resources of a scaled institution.
For the right person, this is a chance to step beyond narrow big-tech specialization and work across the full lifecycle of meaningful machine learning systems — from paper to prototype to production.
About People In AI
People In AI is a specialist recruitment partner focused exclusively on AI, machine learning, data, and infrastructure talent. We partner with some of the most ambitious AI teams globally, helping them identify and hire exceptional technical talent across research, engineering, product, and leadership roles.
