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Machine Learning Quant Jobs in New York (NOW HIRING)

Experience with Machine Learning techniques is a plus * PhD or exceptional Masters / Bachelors ... quantitative subject * Expertise in Python and Linux environments * Comfortable proficiency in C ...

MS or PhD in a quantitative discipline is nice but certainly not required - strong bias for ... Understanding of both modern and classic machine learning techniques * Equally comfortable with ...

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Machine Learning * Factor Models Mock Interviews Conduct realistic mock interviews including: * Technical interviews * Quant brainteasers * Probability questions * Coding interviews * Behavioral ...

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Machine Learning * Factor Models Mock Interviews Conduct realistic mock interviews including: * Technical interviews * Quant brainteasers * Probability questions * Coding interviews * Behavioral ...

Senior Machine Learning Engineer (Remote)

New York, NY · On-site +1

$114K - $157K/yr

A postgraduate degree in Machine Learning, Mathematics, Computer Science, or a related quantitative field * 5 + years experience in Python * 3+ years experience in machine learning research, with a ...

As a ML Engineer, you will support the implementation of diverse Generative AI and Machine Learning ... quantitative discipline 2. 3-5 years of hands-on experience in AI Solution development 3. Basic ...

Bachelor's or Master's degree in Computer Science, Machine Learning, or a related quantitative field. * Minimum 2 years of experience in industry with a strong focus on ML solutions development and ...

Familiarity with machine learning libraries and techniques * Ability to manage multiple competing ... Team-based quantitative/automated trading experience * Knowledge of complex financial products and ...

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Machine Learning Quant information

See New York salary details

$57.4K

$130.4K

$215K

How much do machine learning quant jobs pay per year?

As of Jul 7, 2026, the average yearly pay for machine learning quant in New York is $130,371.00, according to ZipRecruiter salary data. Most workers in this role earn between $85,900.00 and $166,800.00 per year, depending on experience, location, and employer.

What is a Machine Learning Quant job?

A Machine Learning Quant is a specialist in quantitative finance who applies machine learning techniques to develop trading strategies, manage risk, and analyze financial data. They leverage statistical models, deep learning, and reinforcement learning to identify patterns in market data and optimize predictions. This role typically involves programming in Python or C++, working with large datasets, and collaborating with traders and researchers. Machine Learning Quants are employed by hedge funds, investment banks, and proprietary trading firms to gain a competitive edge in financial markets.

What are the key skills and qualifications needed to thrive in the Machine Learning Quant position, and why are they important?

To thrive as a Machine Learning Quant, you need strong skills in quantitative analysis, programming (often in Python or C++), statistical modeling, and a solid foundation in applied mathematics, typically supported by a degree in a quantitative field such as mathematics, physics, computer science, or engineering. Familiarity with machine learning frameworks (like TensorFlow or PyTorch), financial data platforms, and certifications such as CFA or advanced degrees can be advantageous. Critical thinking, collaboration, and clear communication are key soft skills that enhance effectiveness in working with both technical and non-technical stakeholders. These competencies are crucial for building and validating models that inform high-stakes financial strategies and deliver value in fast-paced trading environments.

What are typical daily responsibilities for a Machine Learning Quant in a financial firm?

As a Machine Learning Quant, your day often involves researching and developing predictive models using large financial datasets, backtesting quantitative strategies, and optimizing algorithms for speed and accuracy. You'll collaborate closely with traders, data engineers, and other quants to implement models in live trading environments and refine them based on performance feedback. Regular activities also include monitoring new data sources, adjusting to changes in the market, and documenting your methodologies for regulatory or team review. This multidisciplinary work environment offers the opportunity to continuously learn and directly impact trading outcomes.

What are the most commonly searched types of Machine Learning Quant jobs in New York? The most popular types of Machine Learning Quant jobs in New York are:
Technical Program Manager | Machine Learning

Technical Program Manager | Machine Learning

StaffRight Associates, LLC

Manhattan, NY • On-site

$142K - $184K/yr

Other

Posted 4 days ago


Job description

Preface

The architectural integrity of global investment and technology research relies on the seamless convergence of Machine Learning and Technical Program Management and rigorous computational execution. Within this domain, where the integration of complex data and high-performance systems is the primary objective, the necessity for first-principles mastery extends beyond the laboratory and into the operational nucleus of the organization.

StaffRight Associates is seeking an elite professional to join the Machine Learning Research team at a world-class firm dedicated to the advancement of computational excellence. This role demands a candidate who possesses the intellectual pedigree required to navigate the complex challenges of a research-intensive environment. By bridging sharp technical acumen with proactive problem-solving, the successful candidate will ensure the structural alignment of the firm’s strategic mission with its technical execution.

The Mission

As a Technical Program Manager | Machine Learning, you will serve as a vital catalyst within a premier organization. The mission is to decouple systemic friction from analytical progress, allowing the firm’s leadership to maintain a singular focus on the acceleration of Drug Discovery and Biomolecular Simulation.

You will be tasked with the sophisticated management of complex machine learning projects and scientific dataset acquisition, transforming abstract organizational needs into formalized, actionable results. This role requires a high degree of technical literacy to effectively support a team leveraging proprietary architectures and sophisticated algorithms to revolutionize Drug Discovery and Molecular Science.

Core Technical Objectives
  • Orchestrate Systemic Workflows: Formalize and execute complex protocols and high-stakes technical workflows to ensure continuous operational flow for the Machine Learning group.

  • Distill Complex Data: Conduct ad hoc technical and market research projects, translating multifaceted information into coherent frameworks—such as managing the acquisition of crucial scientific datasets—that support strategic decision-making.

  • Optimize Operational Frameworks: Validate and process intricate documentation and technical requirements with meticulous attention to detail and systemic accuracy across strategic, risk-mitigation, and infrastructure projects.

  • Engineer Multi-Project Solutions: Independently manage a diverse portfolio of concurrent ML projects (including generative models, 3D structure prediction, and LLMs), applying a proactive "Goal-Execution-Mapping" (GEM) approach to solve emergent bottlenecks.

  • Facilitate Elite Communication: Serve as a discreet and articulate interface between the technical committee, internal research leads, and external vendors or stakeholders, maintaining the highest standards of professional integrity.

Candidate DNA
  • Architectural Philosophy: A mindset rooted in efficiency and resilience, with the ability to navigate a hybrid, high-pressure environment with autonomous precision (utilizing a Tuesday–Thursday in-office model in NYC).

  • Resourceful Problem-Solving: A proven track record of execution, demonstrating the ability to anticipate systemic bottlenecks in data pipelining or project dependencies before they impact the broader workflow.

  • Communication Precision: Exceptional interpersonal skills characterized by clarity, discretion, and the ability to seamlessly interact with elite scientific and business minds, bridging the gap between deep ML research and operational execution.

  • Cross-Functional Agility: An adaptive execution style capable of pivoting effortlessly between rigorous operations and technical inquiry within a high-performance ecosystem.

Academic & Research Pedigree
  • Educational Foundation: An advanced degree is preferred, ideally within a STEM discipline or a quantitative field directly relevant to Computer Science, Machine Learning, or Computational Biophysics.

  • Domain Expertise: Prior professional engagement within high-performance computing, quantitative finance, or advanced technology sectors is highly advantageous, particularly experience coordinating complex technical projects or interdisciplinary teams.

  • Quantitative Literacy: Comfort with the mathematical and operational rigor inherent in a firm focused on supercomputing and high-speed data processing.

Partnering with StaffRight Associates

At StaffRight Associates, we operate at the intersection of technical alignment and structural execution. We don’t just match resumes to keywords; we map your engineering DNA, your architectural philosophy, and your approach to system resilience to the most sophisticated Unique STEM Challenges in the industry.

When you partner with us, you are engaging with an elite team that speaks your language and understands the nuances of high-stakes innovation. We are committed to placing elite talent where their technical contributions drive systemic impact.