Machine Learning Research Engineer

Posted yesterday

career techniquesNew York (NY)

Computer and Information Research Scientists Computer Systems Design Services

SENIORITY

Lead

SALARY

$200,000 - $300,000 per year

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About the role

You will focus on expanding our ML research platform to benchmark, rapidly prototype, and stress-test both software and hardware layers across our entire distributed ML stack. By leveraging AI agents and auto-research capabilities, you will push our systems to their limits, identify bottlenecks, and create a frictionless environment to test novel machine learning models on realistic, large-scale data.

Responsibilities: Platform Validation & Infrastructure Benchmarking:

Serve as the primary feedback loop for the entire ML stack.
Actively run complex models through our full ML pipeline to comprehensively test both the training and inference environments.
Validate the central infrastructure in practice, seeing exactly how new research ideas fare and identifying system bottlenecks before broader rollout to research teams.
Streamline Rapid Prototyping for ML Research:
Build high-level abstractions that allow users to bypass setup friction.
Integrate our core ML tooling directly with our underlying simulation and data frameworks, providing a unified entry point to access our full tech stack.
Enable rapid iteration on real-world data and seamless distributed training via Ray.
Agentic Workflows for ML Research:
Leverage AI agents and auto-research workflows to autonomously generate experiments, stress-test our distributed clusters, and provide data-driven, actionable feedback on what infrastructure needs to be optimized or built next.
Research Platform Feedback & Insights Sharing:
Act as the critical bridge between infrastructure builders and ML researchers.
Be the first to exhaustively test new models and push the platform's limits.
Document and publish empirical findings on system capabilities and hardware performance.
Take your validated insights to assist engineering teams with platform improvements and advise researchers on how to best leverage the stack.

Qualifications: Strong Software Engineering Foundation:

Deep proficiency in Python and software design principles.
Ability to build clean, scalable APIs and abstractions that other developers and researchers are enthusiastic about using.
Applied Machine Learning:
Hands-on experience with modern frameworks (PyTorch, TensorFlow, etc.)Strong practical understanding of how to train, evaluate, and deploy models at scale.
Distributed Compute:
Experience scaling ML workloads across GPUs and multi-node clusters using frameworks like Ray, Dask, or PyTorch Distributed.
AI Agent Workflows:
Familiarity with LLM tooling, agentic frameworks, and using AI to automate coding, research, or testing tasks.
System Profiling & Optimization:
Ability to debug and identify bottlenecks across hardware and software layers (e.g., memory limits, GPU utilization, data pipeline latency).Comp: $200-300K + Bonus

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