Machine Learning Performance Engineer (Inference)

New Yorkfull time

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$200k–$300k

H1B: yes

Tower Research Capital Machine Learning Performance Engineer at Tower Research Capital will architect inference pipelines operating at hardware limits to achieve microsecond-level latency. The role involves benchmarking inference platforms and optimizing system architecture for deep memory hierarchies.

Responsibilities

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.
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:

Qualification

Strong Software Engineering FoundationAbility to build cleanApplied Machine LearningDistributed ComputeAI Agent WorkflowsFamiliarity with LLM toolingSystem Profiling & OptimizationAt Tower

Required

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).

Preferred

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.
Anticipated annual base salary range $200,000-$300,000, plus eligible for discretionary bonus.
Tower’s headquarters are in the historic Equitable Building, right in the heart of NYC’s Financial District and our impact is global, with over a dozen offices around the world.
At Tower, we believe work should be both challenging and enjoyable. That is why we foster a culture where smart, driven people thrive – without the egos. Our open concept workplace, casual dress code, and well-stocked kitchens reflect the value we place on a friendly, collaborative environment where everyone is respected, and great ideas win.

Benefits

PTO
Wellness

Company

Tower Research Capital

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