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Applied Machine Learning Systems Engineer
A well-capitalized financial institution in New York City seeks an Applied Machine Learning Systems Engineer to bridge cutting-edge research and production-grade systems. This confidential, on-site role offers the chance to build high-performance ML infrastructure within a small, senior team where every prototype sees real-world impact. Top-tier compensation and unmatched technical challenges await the right builder.
LocationNew York, New York, USA
Comp$500,000 - $1,500,000 per year
Work StyleNo Remote
Role Snapshot
CompanyConfidential Employer
LocationNew York, New York, USA
IndustryFinancial Services
DegreeNot specified
Compensation$500,000 - $1,500,000 per year
Opportunity
Imagine a role where you get to implement the latest machine learning techniques at scale, backed by the resources of a deeply capitalized institution. This is a unique chance to join a specialized engineering group that treats technology as a core business driver, not a cost center. You will work on proprietary data sets that most engineers only dream of and have the compute power to run wild with your best ideas.
Your primary focus will be bridging the gap between research and reality. You will write custom GPU kernels, optimize memory allocation, and build training pipelines that allow researchers to test ideas at unprecedented speed. This isn't academic exploration-every prototype you create is aimed at production use, tackling real financial challenges with massive data flows and low-latency requirements.
The ideal engineer combines deep technical chops with a builder's mentality. You've shipped ML systems that run reliably at scale, and you're not above getting your hands dirty with low-level performance tuning. You stay current with arxiv papers because you genuinely love the craft, and you know how to partner with researchers to turn speculative models into hardened code.
This opportunity offers compensation at the very top of the market, including a base salary of $250K-$350K, a guaranteed first-year bonus, and a sign-on. Add to that a comprehensive benefits package and relocation support, and you have a rare combination of financial reward and technical challenge. If you're ready to work on hard problems that matter immediately, apply today to learn more about this confidential opening.
Non-Negotiables
You have built machine learning systems that successfully went to production, demonstrating the reliability and scale that goes beyond notebook experiments You are comfortable working at the hardware level, including GPU programming, memory optimization, and custom kernel development You can move fast without cutting corners, balancing rapid prototyping with sound engineering principles You have partnered with researchers before and can translate theoretical ideas into running systems that prove or disprove their practical value You stay current with machine learning research out of genuine curiosity and actively apply new techniques to real problems You hold a Bachelor's degree or higher in Computer Science, Machine Learning, Mathematics, or a closely related quantitative field What's In It For You
Earn a base salary of $250,000 to $350,000, plus a guaranteed first-year bonus that can multiply your base and a generous sign-on bonus Receive top-tier medical and prescription coverage, along with wellness reimbursement and family-building support Secure your financial future with a 401(k) matching program that accelerates your long-term savings Relocation assistance is provided to make your move to New York seamless and hassle-free Work on massive, proprietary datasets with an effectively uncapped compute budget-your best ideas will never be hardware-limited Join a team of elite engineers where your contributions directly shape technical direction and are deployed within weeks, not months Enjoy charitable gift matching, empowering you to support causes that matter to you Responsibilities
Spend significant time on low-level performance optimization, writing custom GPU code and eliminating bottlenecks that hinder model training at scale Build the first working prototype of promising new techniques identified by the research team and stress-test them against real-world data Own the design of training infrastructure, enabling researchers to run more experiments faster while keeping compute costs under control Transition prototypes into production systems that run continuously, ensuring the long-term reliability of what you build in weeks Have a real voice in technical direction-you are expected to identify better approaches, make your case, and build the proof Collaborate closely with a small, senior team where everyone ships, with no bureaucratic layers between you and key decisions Preferred Qualifications
You have experience building and optimizing distributed training systems for deep learning at scale You are proficient with large-scale data pipeline tools and frameworks that handle complex preprocessing and transformation You have worked extensively with Python, PyTorch, JAX, and CUDA, and you are comfortable across multiple GPU computing environments An advanced degree (Master's or PhD) in a quantitative field is a strong plus A background in finance or experience with high-frequency, large-scale data applications would set you apart Contributions to open-source machine learning projects or publications in top-tier venues are a bonus, though not required
Role Details & Location
Employment TypeFull Time/Direct Hire
Remote StatusNo Remote
Work Experience3
Total Openings1
Location StringNew York, New York, USA
ZIP11576
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