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Machine Learning Research Engineer

Career Techniques
New York, NY, United States
5 days ago
Apply on www.juju.com
Prepare application

Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Compensation
$200,000 - $300,000
Working hours
Regular working hours
Job source
www.juju.com

Tech stack

Artificial Intelligence Profiling Software Debugging Python (Programming Language) Machine Learning Rapid Prototyping Process Tensorflow Software Engineering Web Application Frameworks Graphics Processing Unit (GPU) Pytorch Large Language Models
+9 more
Agentic-AI Solid Principles Data Analytics Dask Integration Frameworks Machine Learning Operations Virtual Agents Restful APIs Data Pipelines

Job description

  • 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.

Requirements

  • 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).

Apply for this position

This job is hosted externally. Click below to view the full posting and apply.

Apply on www.juju.com
Prepare application

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