Member of Technical Staff — AI LabNYC

People In AI New York City Metropolitan Area
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AI Summary

Join a small, elite AI lab tackling high-stakes problems with real-world impact. Own the full ML lifecycle from idea to production. Collaborate closely with research scientists to choose methods, design experiments, and interpret results.

Key Highlights
Own the full ML lifecycle from idea to production
Collaborate closely with research scientists to choose methods, design experiments, and interpret results
Help evolve distributed ML infrastructure across Ray, Spark, Kubernetes, and cloud-native systems
Key Responsibilities
Own the full ML lifecycle from idea to production: data prep, training, diagnostics, iteration, deployment, and monitoring
Collaborate closely with research scientists to choose methods, design experiments, and interpret results
Build reliable, maintainable production code in Python and modern ML tooling
Partner with research scientists to prototype new modeling ideas, evaluate them rigorously, and productionize successful approaches
Help evolve distributed ML infrastructure across Ray, Spark, Kubernetes, and cloud-native systems
Improve performance across data loading, training loops, and inference paths, with a focus on latency, memory, throughput, and reliability
Design logging, diagnostics, and evaluation checks that make model improvements explainable, durable, and production-ready
Contribute to systems that support high-frequency iteration while meeting real-world SLAs
Technical Skills Required
Python Machine Learning Distributed Training
Benefits & Perks
$1.5 million (Base + Bonus + equity)
100% covered health premiums
Strong retirement matching
Paid parental leave
Wellness resources
On-site gyms
Free meals
Family-building support
Visa sponsorship availability

Job Description


Member of Technical Staff — AI Lab

NYC - 4 days in office

$1.5 million (Base + Bonus + equity)


About the lab

Join a small, elite AI lab tackling high-stakes problems with real-world impact. The team blends startup speed with serious resources. Engineers and researchers work side by side, moving fast, giving direct feedback, and shipping systems that matter.


What you’ll do

  • Own the full ML lifecycle from idea to production: data prep, training, diagnostics, iteration, deployment, and monitoring
  • Collaborate closely with research scientists to choose methods, design experiments, and interpret results
  • Build reliable, maintainable production code in Python and modern ML tooling
  • Partner with research scientists to prototype new modeling ideas, evaluate them rigorously, and productionize successful approaches.
  • Help evolve distributed ML infrastructure across Ray, Spark, Kubernetes, and cloud-native systems.
  • Improve performance across data loading, training loops, and inference paths, with a focus on latency, memory, throughput, and reliability.
  • Design logging, diagnostics, and evaluation checks that make model improvements explainable, durable, and production-ready.
  • Contribute to systems that support high-frequency iteration while meeting real-world SLAs.


What You’ll Bring

  • Strong software engineering fundamentals and experience shipping ML systems into production.
  • Hands-on experience across classical ML and deep learning, with good judgement around when to start simple and when to scale complexity.
  • Familiarity with distributed training, batch and real-time ML workflows, and low-latency serving.
  • Experience debugging production incidents, improving reliability, and building observable systems.
  • Comfort working closely with researchers in a fast-paced, high-feedback environment.
  • Ability to communicate technical decisions clearly and explain the story behind model performance improvements.



Why Join?

This is a rare opportunity to work on AI systems where model quality, engineering execution, and real-world outcomes are tightly connected. You’ll join a small, elite team of researchers and engineers building models that operate directly in live markets, not simply tools that advise human decision-makers.


The environment is highly collaborative, intellectually demanding, and fast-moving. Engineers and scientists work as peers, moving ideas from paper to production with a high degree of ownership. The team also works closely with major cloud and distributed computing ecosystems, often pushing beyond open-source defaults when performance demands it.

Benefits include 100% covered health premiums, strong retirement matching, paid parental leave, wellness resources, on-site gyms, free meals, family-building support, and visa sponsorship availability.


About People In AI

People In AI is a specialist recruitment partner for high-growth AI, machine learning, and infrastructure teams. We work with some of the most ambitious companies and research-led organizations in the market, helping them hire exceptional technical talent across AI research, ML engineering, data infrastructure, and applied AI.


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