Top 10 Foundation models quantization Jobs in New York, Ny

Machine Learning Research, RF Foundation Models Specialist
Distributed Spectrum —New York, NY
- $200,000 - $300,000 a year
- Full-time
- Office
- Math
- Senior level
- Health insurance
- Dental insurance
ML Research Scientist -Deep Learning & Transformer Architectures
Millennium Management —New York, NY
- $150,000 - $200,000 a year
- Email
- Computer science
- Computer Science
- Math
AI Foundation Model Engineer
NTT DATA —Jersey City, NJ
- $80 - $88 an hour
- Hybrid work
- Azure
- Office
- Continuous improvement
- Health savings account
- AD&D insurance
Quick Apply
3d
Machine Learning Engineer - Inference / Serving
Yobi —New York, NY
- $180,000 - $275,000 a year
- Full-time
- Remote
- System design
- Java
- C++
- Dental insurance
- Vision insurance
AI Foundational Model Engineer
NTT Ltd —Jersey City, NJ
- $139,872 - $209,808 a year
- Temporary
- Azure
- Continuous improvement
- Senior level
- Health savings account
- AD&D insurance
3d
Research Engineer, Knowledge Graph Intelligence
Point72 —New York, NY
- $175,000 - $275,000 a year
- Office
- Master's degree
- Analysis skills
- Health insurance
- Tuition reimbursement
Compliance, New York, Vice President, AI/ML Engineer
Goldman Sachs —New York, NY
- $130,000 - $250,000 a year
- Computer Science
- Office
- Master's degree
- Disability insurance
- Health insurance
Applied Researcher I
Capital One —New York, NY
- Email
- Computer Science
- Office
- AWS
- Health insurance
Senior ML Engineer
Invoca —New York, NY
- $152,000 - $228,000 a year
- Remote
- Computer Science
- 5 years
- Bachelor of Science
- Paid holidays
- Health insurance
Quick Apply
Applied Researcher II (AI Foundations)
Capital One —New York, NY
- Email
- Computer Science
- Mid-level
- Office
- Health insurance

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Based on your search for foundation models quantization

ML Research Scientist -Deep Learning & Transformer Architectures
Millennium Management —New York, NY
The successful candidate will have a PhD in machine learning or a related field and demonstrated ability to implement Transformer architectures from first…
$150,000 - $200,000 a year
Machine Learning Research, RF Foundation Models Specialist
Distributed Spectrum —New York, NY
Use field performance to inform the next generation of models and tooling. Clear technical communication and cross-functional collaboration.
$200,000 - $300,000 a year

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Machine Learning Research, RF Foundation Models Specialist

Distributed Spectrum
New York, NY

Apply Now

Job Details

Full-time
$200,000 - $300,000 a year

Benefits

Health insurance
Dental insurance
Paid time off
Vision insurance
401(k) matching

Qualifications

Computational framework
Computational modeling
Math
Machine learning (ML) fundamentals
Cross-functional collaboration
Machine learning frameworks
Cross-functional communication

Full Job Description

DS creates systems that power the next generation of radio spectrum intelligence. We collect radio data from all over the world, train neural networks to decipher it, and run them on the smallest chips we can. We’re solving a new, technically hard problem where nothing from other fields works out of the box, and along the way, we’ve built our own stack from scratch, including entirely new embedding model architectures, custom GPU kernels, and much more.

Joining DS means owning major parts of a fast-growing AI research organization, joining a collaborative, talent-dense team with decades of experience in probabilistic ML, accelerated computing, embedded systems, and signal theory, and growing your career in the areas that interest you. You’ll fit in if you want to come to work for the problem itself and don’t want to choose between technical rigor, business value, and real-world impact.

We work with high ownership and trust, and we do it together in the office 5 days/week.

About the Role

Some domains already have standard ML playbooks. RF is not one of them.

Distributed Spectrum is building AI-enabled sensing systems for the radio domain, and we are hiring a Machine Learning Researcher, Specialist to bring modern ML to a problem space where representation, structure, physics, runtime constraints, and deployment realities all matter at once.

This role is designed for a strong generalist researcher who wants genuinely open technical terrain. You will work on problems where signal structure, propagation effects, interference, sparse visibility, and edge deployment constraints all shape what "good" looks like. The job is not just to improve accuracy. It is to formulate the right problem, find the right modeling approach, and get that capability into systems that are used in the field.

You will work across the lifecycle of research and deployment: data and evaluation design, experimentation, model development, release readiness, and iteration based on real-world outcomes. You will collaborate closely with embedded, hardware, and mission teammates, and your work will directly influence how Distributed Spectrum builds machine learning capability as the company scales.

What You'll Do

Formulate new ML problems in RF sensing and spectrum understanding
Design experiments and evaluation approaches that reflect real operating conditions including domain shift, changing interference, and varying sensors and platforms
Build models for structured, noisy, and partially observed signal environments
Improve robustness across propagation, interference, and low-visibility waveform conditions
Optimize models for throughput, latency, and deployment constraints
Move promising research into a release path for real systems through proofs-of-concept, realistic validation, and conversion into maintainable, deployable code
Use field performance to inform the next generation of models and tooling

What We're Looking For

Deep mathematical and modeling fundamentals
Strong hands-on experience with modern ML frameworks and experimental practice
Ability to work in domains where problem formulation is as important as implementation
Strong instincts for signal-rich, structured, non-generic data
Comfort operating with ambiguity and changing requirements
Clear technical communication and cross-functional collaboration

Nice-To Haves

Background in RF or signal-centric ML (spectrum sensing, modulation recognition, or related work) is welcome but not required; we are equally interested in researchers from adjacent domains who have demonstrated strong reasoning on hard signal or sensing problems
Experience building for constrained inference (quantization, kernel-level optimizations, or similar)
Evidence of research impact: publications, open-source implementations, or prior work building new architectures that shipped

Who Thrives at Distributed Spectrum

Fast learners over specific backgrounds – We care more about how quickly you can pick up new skills than where you’ve worked before.
Intellectual honesty – The right answer matters more than being right. You challenge assumptions, test ideas, and pivot when needed.
Adaptability – We’re organized, but sometimes things change quickly. You find a way to make it work and balance short-term deliverables with long-term goals.
Ownership of outcomes – You optimize your own time, focus on what matters to deliver quickly, and cut out inefficiencies.
Not building in a vacuum – You stay connected to the rest of our teams and our customers to make sure all the pieces fit together.

What We Offer

Above-market salary, equity, and benefits package.
Early Series A Equity
Excellent health, dental, and vision coverage
401(k) match - up to 4% of your salary
Flexible PTO
Daily office lunches in NYC

ITAR Requirements

To conform to U.S. Government technology export regulations, including the International Traffic in Arms Regulations (ITAR) you must be a U.S. citizen, lawful permanent resident of the U.S., protected individual as defined by 8 U.S.C. 1324b(a)(3), or eligible to obtain the required authorizations from the U.S. Department of State. Learn more about the ITAR here.

Compensation Range: $200K - $300K

Apply Now

Refine Your Search

foundation models quantization jobs in new york, ny

Machine Learning Research, RF Foundation Models Specialist

ML Research Scientist -Deep Learning & Transformer Architectures

AI Foundation Model Engineer

Machine Learning Engineer - Inference / Serving

AI Foundational Model Engineer

Research Engineer, Knowledge Graph Intelligence

Compliance, New York, Vice President, AI/ML Engineer

Applied Researcher I

Senior ML Engineer

Applied Researcher II (AI Foundations)

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