Machine Learning Engineer

Riviera Partners; New York (NY); On-site; USD 160,000 - 230,000; Full time

Job summary

Riviera Partners is recruiting on behalf of an independent AI research lab affiliated with a premier global investment firm in New York City. The role is hands-on and expects deep ML expertise and production experience for on-site work.

As a Machine Learning Engineer, you will design, train, and deploy production-grade ML models, build scalable pipelines, and collaborate with researchers to translate investment hypotheses into robust systems while maintaining a strong software engineering

Qualifications

• Strong ML foundations with tabular/time-series modeling knowledge.
• Experience taking models from data through training to production.
• Solid software engineering practices for reliable, observable systems.
• Strong mathematical background in probability, statistics or linear algebra.
• Experience with distributed systems or performance profiling is a plus.

Responsibilities

• Design, train, and deploy production ML models end-to-end from data to monitoring.
• Build and maintain scalable ML pipelines for structured data.
• Run offline/online experiments to validate model improvements before production.
• Optimize models for latency, throughput, and reliability in production.
• Collaborate with quantitative researchers to translate investment hypotheses into ML systems.
• Work within an independent lab structure with focused research mission.

Skills

ML foundations

End-to-end deployment

Software engineering depth

Quantitative modeling

Systems thinking

Education

BS/MS/PhD in CS/EE/Math/Physics

Job description

Machine Learning Engineer — AI Investment Research Lab

Location: New York, NY (on-site) Level: Mid to Senior (3–10+ years) Type: Full-time Posted by: Riviera Partners (recruiting on behalf of our client

About the Role

Our client is an independent AI research lab — operating with its own leadership, research agenda, and engineering culture — affiliated with a premier global investment firm. Their mission is to build machine intelligence that understands and predicts markets, and they are doing it with a small, highly selective team where individual contributors have real influence over the technology and direction. This is a hands-on individual contributor role. Everyone on the team writes code daily. They are not looking for technical leads who have stepped back from the work — they want engineers who go deep.

What You'll Do

• Design, train, and deploy production ML models end-to-end — from raw data through training, serving, and monitoring
• Build and maintain scalable ML pipelines for structured, tabular, and time-series data
• Run rigorous offline and online experiments (A/B testing, causal inference) to validate model improvements before production deployment
• Optimize models for latency, throughput, and reliability in production environments
• Collaborate with quantitative researchers to translate investment hypotheses into ML systems
• Work within an independent lab structure with dedicated leadership and a focused research mission

What We're Looking For

Strong foundation in classical ML and tabular/time-series modeling. You're comfortable with gradient boosted trees, feature engineering at scale, statistical modeling, and forecasting. You know when a well-tuned XGBoost beats a transformer — and why.

End-to-end production ownership. You've built systems that went from data through training to production serving and monitoring. You can speak to specific architectural decisions, tradeoffs, and failure modes — not just impact metrics.

Software engineering depth. You started as a software engineer or built strong SWE instincts alongside your ML work. You think about models as production systems that need to be reliable, observable, and maintainable.

Quantitative horsepower. Strong mathematical foundations — probability, statistics, linear algebra, optimization. Physics, applied math, or computational science backgrounds welcome.

Systems-level thinking. Bonus for experience with distributed training, inference optimization, CUDA, or performance profiling. We value engineers who go below the framework API when the problem requires it.

This Role Is Probably Not for You

• If...Your recent ML work is primarily RAG pipelines, prompt engineering, LangChain, or chatbot development with no model training underneath
• You've moved primarily into people management and are no longer hands-on with code daily
• Your experience is deploying and integrating pre-trained models rather than designing and training them
• Your ML work has been consulting across many clients rather than deep ownership of a single system over time

Preferred Background

• 3–10+ years of ML engineering experience with a track record of shipping production systems
• Strong CS foundation (BS/MS/PhD in CS, EE, Math, Physics, or related quantitative field)
• Experience in domains such as fraud detection, ads ranking, recommendation systems, demand forecasting, or financial modeling — especially with tabular and structured data
• Familiarity with classical ML methods alongside modern deep learning

Why Join Our Client

• Independent lab with its own identity and research direction — not a corporate AI team
• Work on problems that matter at scale: models informing decisions about global markets
• Small, high-caliber team where individual contributors have real influence
• Competitive compensation including meaningful performance-based upside
• Intellectually rigorous environment that values first-principles thinking