Machine Learning Research Engineer

• 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.

• 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.

• 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.
• Distributed Compute:
• 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 Have:

• 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.

Our benefits include:

• 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

At Tower, you’ll find a collaborative and welcoming culture, a diverse team and a workplace that values both performance and enjoyment.

No unnecessary hierarchy.

No ego.

Just great people doing great work – together.

Tower Research Capital is an equal opportunity employer.