Applied ML Systems Engineer - Finance

Park Lane Recruitment; New York (NY); On-site; USD 250,000 - 350,000; Full time

Benefits offered by this job

Relocation support

Sponsorship available

Elite total compensation

401(k) matching

Medical and prescription coverage

Wellness reimbursement

Family building support

Charitable gift matching

Job summary

Park Lane Recruitment is seeking an experienced ML/Infrastructure engineer in New York to build and optimize production ML systems, including training infrastructure and GPU-accelerated workloads.

The role emphasizes collaboration with researchers, rapid prototyping, memory tuning, and durable production deployments. Relocation sponsorship is available, with base salary of $250,000 to $350,000 and a guaranteed first-year bonus plus comprehensive benefits.

Qualifications

• 3 to 15 years of experience in ML engineering or distributed/infrastructure engineering.
• Hands-on ability with Python, PyTorch, JAX, CUDA and GPU computing tools.
• Experience building ML systems that moved to production with training infrastructure and optimization.

Responsibilities

• We build and optimize machine learning systems that move from research ideas into production use.
• We write custom GPU code and tune memory usage to reduce training time and remove performance bottlenecks.
• We design and improve training infrastructure so researchers can run more experiments faster without destabilizing systems or inflating compute costs.
• We develop first‑pass implementations of promising new techniques and validate them against real data.
• We help transition prototypes into durable production systems that can run continuously over time.
• We collaborate closely with researchers to turn conceptual architecture ideas into functioning systems.
• We influence technical direction by evaluating better approaches and building proof‑of‑concept solutions.
• We work across a small, senior team where everyone contributes directly and ships meaningful work.

Skills

Research collaboration

Translating ideas to systems

Education

Background in CS/Math/Physics/EE or quantitative discipline

Tools

Python

PyTorch

JAX

CUDA

Job description

Requirements

• We need proven experience building ML systems that have reached production, with clear evidence of work in training infrastructure, distributed training, GPU optimization, and model performance.
• We are looking for candidates with 3 to 15 years of experience in ML engineering, infrastructure engineering for ML systems, or distributed/performance engineering.
• We expect strong hands‑on ability with Python, PyTorch, JAX, CUDA, and other GPU computing tools.
• We need someone comfortable working close to the hardware, including custom kernels, memory optimization, and low‑level performance tuning.
• We value a background in computer science, machine learning, mathematics, physics, electrical engineering, or another quantitative discipline.
• We prefer candidates with a strong academic pedigree, advanced coursework, research exposure, and evidence of building substantial systems and projects.
• We are seeking people who have experience partnering with researchers and translating ideas into working systems.
• We need someone who can prototype quickly while still building solutions properly and reliably.
• We strongly value curiosity about modern ML research and the ability to identify how new methods can be applied in practice.
• We are especially interested in candidates with experience at leading technology companies, AI labs, top quantitative firms, or high‑end startup environments.

Responsibilities

• We build and optimize machine learning systems that move from research ideas into production use.
• We write custom GPU code and tune memory usage to reduce training time and remove performance bottlenecks.
• We design and improve training infrastructure so researchers can run more experiments faster without destabilizing systems or inflating compute costs.
• We develop first‑pass implementations of promising new techniques and validate them against real data.
• We help transition prototypes into durable production systems that can run continuously over time.
• We collaborate closely with researchers to turn conceptual architecture ideas into functioning systems.
• We influence technical direction by evaluating better approaches and building proof‑of‑concept solutions.
• We work across a small, senior team where everyone contributes directly and ships meaningful work.

Company

We are a highly capitalized, confidential financial institution in midtown Manhattan building a specialized engineering group focused on applying modern machine learning across the organization. We are not a bank or a startup; engineering and research are central to our business. This is an in‑person role in New York with relocation support, sponsorship available, and compensation that includes a base salary of $250,000 to $350,000, a guaranteed first‑year bonus, and sign‑on, with total compensation in the elite tier. We offer comprehensive benefits including 401(k) matching, medical and prescription coverage, wellness reimbursement, family building support, and charitable gift matching. Our team is small, senior, and highly collaborative, with direct influence over what gets built and deployed.