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# Machine Learning Research Engineer

- **Company:** Career Techniques
- **Location:** New York, NY, United States
- **Salary:** $200,000 - $300,000
- **Contract:** Permanent contract
- **Skills:** 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, Agentic-AI, Solid Principles, Data Analytics, Dask, Integration Frameworks, Machine Learning Operations, Virtual Agents, Restful APIs, Data Pipelines
- **Published:** November 7, 2026
- **Apply:** https://www.juju.com/job/28_25196_33c77131ca322e48c9ff7615eaea8113

## About the Role

* 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).

## 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.