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

401(k) matching

Medical coverage

Wellness reimbursement

Family building support

Charitable gift matching

Job summary

Park Lane Recruitment seeks a senior ML/infra engineer for a confidential financial institution in Midtown Manhattan. The role is on-site with relocation support and sponsorship. Expect base salary $250,000–$350,000, plus a guaranteed first-year bonus and sign-on, with total compensation in the elite tier.

The candidate will build production ML systems, optimize training infrastructure, and work closely with researchers to push ideas into scalable, durable implementations.

Qualifications

• Proven experience building ML systems that reached production.
• 3–15 years in ML engineering or distributed/performance engineering.
• Strong hands-on with Python, PyTorch, JAX, CUDA.
• Comfort working near hardware with kernel and memory optimizations.
• Background in quantitative disciplines (CS, math, physics, EE).
• Academic pedigree with substantial systems/projects experience.
• Experience partnering with researchers to ship working systems.
• Ability to prototype quickly while building reliable solutions.
• Curiosity about modern ML research and practical applications.
• Experience at leading tech companies, AI labs, or high-end startups.

Responsibilities

• Build and optimize ML systems moving ideas into production.
• Write custom GPU code and tune memory to reduce training time.
• Design training infra allowing researchers to run more experiments.
• Develop first-pass implementations and validate on real data.
• Transition prototypes into durable production systems.
• Collaborate with researchers to turn concepts into functioning systems.
• Influence technical direction with proof-of-concept solutions.
• Work in a small, senior team delivering meaningful work.

Skills

Python

PyTorch

JAX

CUDA

GPU optimization

Memory optimization

Distributed training

Performance tuning

Education

CS/Math/Physics/EE degree

Tools

CUDA toolkit

Profiling tools

GPU kernels

Job description

Requirements

Must have:

• 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.

• 401(k) matching
• medical and prescription coverage
• wellness reimbursement
• family building support
• charitable gift matching