Machine Learning Research Engineer
About The Position
As an AI/ML Applied Research Engineer, you will sit at the cutting-edge intersection of our central machine learning infrastructure and our research teams. Your core mandate is to act as "Customer Zero" for our internal ML Research platform. 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. Ultimately, by hands-on testing these systems yourself, you will act as a technical advisor. You will share insights on research progress, evaluate how new ideas fare in practice, and help guide the strategic direction of our central engineering efforts.
Requirements
- 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.
- Hands-on experience with modern frameworks (PyTorch, TensorFlow, etc.)
- Strong practical understanding of how to train, evaluate, and deploy models at scale.
- Experience scaling ML workloads across GPUs and multi-node clusters using frameworks like Ray, Dask, or PyTorch Distributed.
- Familiarity with LLM tooling, agentic frameworks, and using AI to automate coding, research, or testing tasks.
- Ability to debug and identify bottlenecks across hardware and software layers (e.g., memory limits, GPU utilization, data pipeline latency).
Nice To Haves
- Previous experience working in quantitative finance or complex algorithmic research environments.
- Familiarity with large-scale time-series data, simulation engines, or performance benchmarking.
- A proven track record of bridging the gap between systems engineering and applied machine learning research.
Responsibilities
- 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.
- 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.
- 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.
- 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.
Benefits
- Generous paid time off policies
- Savings plans and other financial wellness tools available in each region
- Hybrid working opportunities
- Free breakfast, lunch, and snacks daily
- In-office wellness experiences and reimbursement for select wellness expenses (e.g., gym, personal training and more)
- Company-sponsored sports teams and fitness events (JPM Corporate Challenge, Cycle for Survival, Wall Street Rides FAR and more)
- Volunteer opportunities and charitable giving
- Social events, happy hours, treats, and celebrations throughout the year
- Workshops and continuous learning opportunities
Stand Out From the Crowd
Upload your resume and get instant feedback on how well it matches this job.
What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
No Education Listed
