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Operations Research Engineer Jobs in New York (NOW HIRING)

Normal Computing

Research Engineer, Algorithms

New York, NY · On-site

$300K - $400K/yr

It is rethinking how operations like attention, memory access, and long-context decoding behave ... Strong programming skills in Python and at least one systems language * Collaborative instinct and ...

Amperos Health, Inc

AI Research Engineer

New York, NY · On-site

$200K - $300K/yr

About the role As an AI Engineer on our research team, you'll work on our hardest research and AI ... Worked with complex distributed systems with many async operations * Excited about pushing the ...

Peraton

Senior Research Engineer

Red Bank, NJ · On-site

$107K - $147K/yr

Responsibilities Peraton is seeking a Senior Research Engineer to help drive an established program ... Own feature and product definition end to end translating user needs and operational context into ...

Peraton

Senior Research Engineer

Red Bank, NJ · On-site

$135K - $216K/yr

Responsibilities Peraton is seeking a Senior Research Engineer to help drive an established program ... Own feature and product definition end to end translating user needs and operational context into ...

LIGHTERA

R&D Engineer

Somerset, NJ · On-site

Support the development, operation, and continuous improvement of research fabrication processes ... Knowledge of engineering principles related to mechanical systems, motion control, or fluid ...

Peraton

Senior Research Engineer

Red Bank, NJ · On-site

$107K - $147K/yr

Responsibilities Peraton is seeking a Senior Research Engineer to help drive an established program ... Own feature and product definition end to end translating user needs and operational context into ...

Lightera

R&D Engineer

Somerset, NJ

Support the development, operation, and continuous improvement of research fabrication processes ... Knowledge of engineering principles related to mechanical systems, motion control, or fluid ...

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How much do operations research engineer jobs pay per year?

As of Aug 8, 2026, the average yearly pay for operations research engineer in New York is $93,024.00, according to ZipRecruiter salary data. Most workers in this role earn between $76,000.00 and $102,800.00 per year, depending on experience, location, and employer.

What does an operations research engineer do?

An Operations Research Engineer applies mathematical modeling, statistical analysis, and optimization techniques to solve complex business and engineering problems. They use data-driven decision-making to improve efficiency, reduce costs, and enhance system performance in industries such as logistics, manufacturing, finance, and transportation. By leveraging algorithms, simulations, and predictive analytics, they help organizations make informed decisions. Their work often involves programming, data analysis, and collaboration with cross-functional teams to implement practical solutions.

What are the key skills and qualifications needed to thrive as an operations research engineer?

To excel as an Operations Research Engineer, you need a solid grounding in mathematics, statistics, optimization, and analytical problem-solving, typically supported by a relevant engineering or quantitative degree. Proficiency with tools such as MATLAB, Python, R, CPLEX, or simulation software, as well as familiarity with data analysis platforms, is commonly required. Strong communication, teamwork, and adaptability are vital soft skills for presenting findings and collaborating across multidisciplinary teams. Mastery of these abilities is crucial to developing effective solutions that improve operational efficiency in complex business environments.

What does a typical day look like for an operations research engineer?

A typical day for an Operations Research Engineer involves identifying operational challenges, gathering and analyzing data, developing mathematical models, and simulating scenarios to propose improvements. You will often collaborate closely with cross-functional teams—including data scientists, engineers, and business managers—to understand requirements and implement optimal solutions. Expect to spend time both independently conducting research and attending meetings to present your findings and receive feedback. The role provides a good balance of technical work, problem-solving, and teamwork, making each day dynamic and intellectually engaging.

What are the most commonly searched types of Operations Research Engineer jobs in New York? The most popular types of Operations Research Engineer jobs in New York are:
What are popular job titles related to Operations Research Engineer jobs in New York? For Operations Research Engineer jobs in New York, the most frequently searched job titles are:
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Operations Research Engineer jobs near you
Infographic showing various Operations Research Engineer job openings in New York as of August 2026, with employment types broken down into 85% Full Time, 12% Part Time, 1% Temporary, and 2% Contract. Highlights an 94% Physical, 2% Hybrid, and 4% Remote job distribution, with an average salary of $93,024 per year, or $44.7 per hour.

Research Engineer, Algorithms

Normal Computing

New York, NY • On-site

$300K - $400K/yr

Full-time

Posted 20 days ago

Job description

About Normal Computing
Normal Computing builds silicon that turns thermal noise from an obstacle into a computational resource. Conventional chips spend most of their energy forcing determinism onto physics; ours compute with it. Stochastic, in-memory, asynchronous: the result is 10-100× more AI inference per dollar, per watt.
We co-design the full stack: AI-native EDA systems in production with the world's largest semiconductor companies, and the advanced ASICs they make possible. Backed by $85M+ from the world's leading deep-tech investors and built by scientists, engineers, and operators from the labs that built modern computing.
Normal works as one team across New York, Silicon Valley, London, Copenhagen, and Seoul. We hire people who want the hardest version of their craft, across every discipline, at every seniority.
The Role
You will develop the computational methods that make AI inference run efficiently on Normal's thermodynamic hardware. The core challenge is not adapting standard GPU kernels to a new chip. It is rethinking how operations like attention, memory access, and long-context decoding behave when the underlying substrate uses stochastic analog computation in memory rather than conventional digital logic.
Normal's ASICs run the heaviest operations of large model inference inside memory itself. Your job is to develop the algorithms that exploit this natively: understand what transformer and diffusion workloads are well-suited to stochastic analog execution, design numerical methods that map onto the hardware's physical dynamics, and validate them against real silicon or high-fidelity simulation.
This is a co-design role. The hardware and the algorithms are developed in parallel, which means you will influence architectural decisions, not just implement against a fixed specification. The strongest candidates have a deep understanding of both large model inference and the mathematics of stochastic systems, and have built systems that run on real hardware, not just in theory.
What You Will Own
  • Algorithm Development: Develop algorithms for transformer inference workloads running on stochastic analog processing-with-memory hardware.
  • Software/Hardware Co-Design: Work directly with hardware and architecture teams to shape what the chip can and should compute natively.
  • Numerical Methods: Design numerical methods that exploit thermal noise and analog dynamics rather than working around them.
  • Evaluation & Benchmarks: Build evaluation frameworks and benchmarks that characterize algorithm behavior on real hardware or simulation.
  • Workload Translation: Translate insights about model workloads into constraints and opportunities for hardware design.
  • Rapid Prototyping: Prototype and iterate rapidly as hardware evolves from simulation to silicon.

What Makes You a Great Fit
  • Deep understanding of large model inference: attention mechanisms, KV cache, long-context decoding, memory bandwidth constraints
  • Experience with inference optimization: quantization, sparsity, kernel fusion, or memory-efficient attention
  • Familiarity with stochastic systems, probabilistic methods, numerical analysis, or analog computation
  • Experience implementing algorithms close to hardware, not just in high-level frameworks
  • Comfort reasoning from first principles about what a novel substrate can do efficiently
  • Track record of taking ideas from theory to working implementation on real hardware
  • Strong programming skills in Python and at least one systems language
  • Collaborative instinct and ability to work across hardware, architecture, and software teams

Bonus Points
  • PhD in machine learning, applied mathematics, physics, electrical engineering, or a related field
  • Exposure to analog or mixed-signal systems, in-memory compute, or non-von-Neumann architectures
  • Experience working on hardware that did not yet exist when you joined
  • Publications or open-source work in efficient inference, stochastic algorithms, or novel computing

Equal Employment Opportunity Statement
Normal Computing is an Equal Opportunity Employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, veteran status, or any other legally protected status.
Accessibility Accommodations
Normal Computing is committed to providing reasonable accommodations to individuals with disabilities. If you need assistance or an accommodation due to a disability, please let us know at accommodations@normalcomputing.com.
Privacy Notice
By submitting your application, you agree that Normal Computing may collect, use, and store your personal information for employment-related purposes in accordance with our Privacy Policy.

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