Machine Learning Engineer (Training & Inference Systems)

Fintal Partners; New York (NY); On-site; USD 185,000 - 230,000; Full time

Job summary

Fintal Partners in New York is hiring engineers to build the distributed training and inference systems that take models from research into production. You’ll work side-by-side with researchers and traders on a trading floor-like environment, owning critical, IC-heavy infrastructure projects.

The role focuses on scaling training/inference at scale, optimizing for latency, and expanding a GPU footprint from thousands to tens of thousands, with top-tier compensation for engineers who can execute

Qualifications

• Experience scaling training and inference infrastructure.
• Deep PyTorch fluency and performance-focused mindset.
• Distributed systems with multi-GPU/multi-node training and model serving.
• Hands-on contributor, not a strategy seat.
• Experience at top tech firm, trading firm, or a strong big-tech + startup combo.
• 3+ years of relevant experience.

Responsibilities

• Build and scale distributed training systems for large models.
• Develop low-latency inference pipelines to production.
• Work directly with researchers training the models and the traders/quants relying on them.
• Operate on a rapidly growing GPU footprint (low thousands today, scaling into the tens of thousands).

Skills

PyTorch

Distributed systems

Performance engineering

Hands-on building

Trading background

3+ years experience

Job description

A leading global trading firm is building out its machine learning function and hiring engineers to develop the distributed training and inference systems that take models from research into production. This is the most urgent hire on a team that sits unusually close to the trading desk — you’ll feel more like part of the trading floor than a traditional ML function.

What you’ll do

• Build and scale distributed training systems for large models
• Develop low-latency inference pipelines to get models into production
• Work directly with researchers training the models and the traders/quants relying on them
• Operate on a rapidly growing GPU footprint (low thousands today, scaling into the tens of thousands over the next few years)

What we’re looking for

• Strong experience building training and/or inference infrastructure at scale
• Deep PyTorch fluency and strong performance-engineering instincts
• Distributed systems experience — multi-GPU/multi-node training, model serving
• A track record of hands-on building (this is an IC-heavy role, not a strategy seat)
• Background at a top tech firm, trading firm, or a strong big-tech + startup combination
• 3+ years of relevant experience; flexible on level for the right person

Why itfff’s exciting

• Real impact, fast. Your systems directly enable production models that drive trading decisions — no research-to-nowhere, no shipping into a void.
• Scale that actually scaling. A GPU footprint growing 10x+ over the next two to three years, with the mandate and budget to match.
• Proximity to the work that matters. Youall sit alongside researchers and traders, not three layers removed. Tight feedback loops, smart people, genuine ownership.
• Top-of-market comp for engineers who can build at this level.