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Applied Science Engineering Jobs (NOW HIRING)

Ramp

Senior Applied Scientist, Credit Risk

New York, NY · On-site

$165K - $228K/yr

Bachelor's degree or above in Math, Economics, Bioinformatics, Statistics, Engineering, Computer Science, or other quantitative fields. * 5+ years of industry experience as an Applied Scientist ...

Oracle

Senior Applied Scientist

Seattle, WA · On-site

$104K - $142K/yr

As a Senior Applied Scientist on the team, you will independently own complex evaluation work from ... Partner with science, engineering, product, data, and operations teams to define requirements ...

Adobe

Manager, Applied Science for Data

Seattle, WA · On-site

As a Manager for Applied Science at Adobe, you will develop novel data-centric solutions that ... Collaborate with researchers, ML engineers, and product managers to align on data needs for ...

Grid

Applied Scientist

Seattle, WA

$120K - $140K/yr

You'll work closely with product, engineering and business leaders to make a difference with data ... Applied Science Expertise: Proven experience in Machine Learning and/or Applied Science, including ...

Grid

Applied Scientist

Seattle, WA

$120K - $140K/yr

You'll work closely with product, engineering and business leaders to make a difference with data ... Applied Science Expertise: Proven experience in Machine Learning and/or Applied Science, including ...

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All JobsApplied Science Engineering Jobs

Applied Science Engineering information

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$40.5K

$98.8K

$156.5K

How much do applied science engineering jobs pay per year?

As of Aug 16, 2026, the average yearly pay for applied science engineering in the United States is $98,759.00, according to ZipRecruiter salary data. Most workers in this role earn between $78,000.00 and $116,000.00 per year, depending on experience, location, and employer.

What is the difference between Applied Science Engineering vs Mechanical Engineering?

AspectApplied Science EngineeringMechanical Engineering
Required CredentialsBachelor's or higher in applied science, engineering, or related fieldsBachelor's or higher in mechanical engineering or related disciplines
Work EnvironmentResearch labs, product development, testing facilitiesManufacturing plants, design offices, testing labs
Employer & Industry UsageTech companies, research institutions, manufacturing firmsAutomotive, aerospace, energy, manufacturing industries
Common Search & Comparison IntentUnderstanding career paths, job roles, and skillsDesign, analysis, and manufacturing processes

Applied Science Engineering focuses on applying scientific principles to develop new technologies and solutions, often emphasizing research and development. Mechanical Engineering, on the other hand, centers on designing and manufacturing mechanical systems. Both roles require similar educational backgrounds and are used across various industries, but their primary focus and work environments differ.

What are the key skills and qualifications needed to thrive as an applied science engineer?

To thrive as an Applied Science Engineer, you need a strong background in mathematics, physics, and engineering principles, typically supported by a degree in engineering or a related science field. Experience with programming languages (such as Python or MATLAB), data analysis tools, and relevant certifications in areas like machine learning or computational modeling are commonly required. Strong problem-solving abilities, teamwork, and effective communication skills help you collaborate and convey complex technical concepts. These skills ensure innovative solutions, accurate analyses, and successful project outcomes in a multidisciplinary engineering environment.

What is applied science engineering?

Applied science engineering is a multidisciplinary field that uses scientific knowledge and engineering principles to develop practical solutions for real-world problems. Professionals in this area bridge the gap between theoretical research and its practical application, working on projects that can range from developing new materials and medical devices to improving environmental systems. They often collaborate with scientists, engineers, and business leaders to translate discoveries into tangible products or processes. Their work can be found in industries such as biotechnology, manufacturing, information technology, and energy.

How do applied science engineers typically collaborate with cross-functional teams during the development of new technologies?

Applied Science Engineers often work closely with product managers, data scientists, and software developers to bridge the gap between scientific research and practical implementation. They contribute their expertise by designing experiments, analyzing data, and translating research findings into scalable solutions. Regular meetings, collaborative brainstorming sessions, and shared project management tools are common, ensuring alignment between scientific objectives and business goals. This collaborative structure not only accelerates innovation but also provides engineers with exposure to diverse perspectives and continuous learning opportunities.
More about Applied Science Engineering jobs

What states have the most Applied Science Engineering jobs?

States with the most job openings for Applied Science Engineering jobs include:

What job categories do people searching Applied Science Engineering jobs look for?

The top searched job categories for Applied Science Engineering jobs are:

Applied Science Engineering jobs near you
Infographic showing various Applied Science Engineering job openings in the United States as of August 2026, with employment types broken down into 91% Full Time, 4% Part Time, 4% Contract, and 1% Nights. Highlights an 86% Physical, 5% Hybrid, and 9% Remote job distribution, with an average salary of $98,759 per year, or $47.5 per hour.

Senior Applied Scientist, Credit Risk

Ramp

New York, NY • On-site

$165K - $228K/yr

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Re-posted 6 days ago

Job description

About Ramp
Ramp is building the smart infrastructure for finance teams, embedded in the transaction flow of every dollar a business spends. We automate how over $200B in annualized spend flows in and out of 70,000+ companies: authorizing payments, flagging risk, categorizing spend, and closing books.
The problems are high-stakes, data-dense, and unforgiving.
We hire people with high agency and high urgency. We look for slope over intercept. We care less about where you trained and more about what you've built. At Ramp, everyone is a builder who owns problems end to end and makes consequential decisions that shape the outcome.
The median Ramp customer saves 5% and grows revenue 16% in their first year - far in excess of businesses operating without Ramp. We believe every ambitious company deserves the same.
If you want to build systems that directly shape how companies move and manage billions, Ramp is the place to do it.
About the Role
We're looking for a Senior Applied Scientist to help drive the future of credit applied science at Ramp. In this role, you will design, build, and optimize the models that power our credit risk systems, helping us make faster, smarter, and more scalable risk decisions for our customers.
You'll work at the intersection of machine learning, statistics, economics, and product strategy. This role requires strong technical depth as well as close collaboration with business, product, data, and engineering partners. You will help identify high-impact opportunities, translate ambiguous business problems into rigorous modeling work, and ship models that operate reliably in production.
Applied scientists at Ramp focus on solving quantitative problems across credit, fraud, growth, and our core product by applying the right mix of machine learning, causal inference, structural modeling, and optimization.
What You'll Do
  • Design, build, and optimize machine learning models that support credit risk decisioning and portfolio management at Ramp
  • Own the full applied science development lifecycle, from data exploration and feature development to model prototyping, deployment, monitoring, and iteration
  • Investigate and evaluate new data sources, including structured and unstructured data, and integrate them into credit models where appropriate
  • Develop backtesting, validation, and monitoring frameworks to evaluate model performance and business impact
  • Apply methods from machine learning, statistics, causal inference, optimization, and economics to solve core business problems
  • Generate and communicate data-driven insights that influence product, risk, and company strategy
  • Partner with product, business, engineering, and data stakeholders to translate ambiguous problems into clear objectives, scoped opportunities, and a practical applied science roadmap
  • Contribute to best practices for model development, experimentation, documentation, testing, and production reliability

What You Need
  • Bachelor's degree or above in Math, Economics, Bioinformatics, Statistics, Engineering, Computer Science, or other quantitative fields.
  • 5+ years of industry experience as an Applied Scientist, Machine Learning Engineer, Research Scientist, or equivalent; or 3+ years of industry experience with a PhD
  • Strong familiarity with the mathematical fundamentals of advanced statistics, machine learning, optimization, and/or economics
  • Experience working with large datasets using Python and SQL
  • Strong Python experience across exploratory data analysis, predictive modeling, and applied machine learning, using tools such as NumPy, pandas, scikit-learn, PyTorch, or similar libraries
  • Strong communication: the ability to bridge technical methodology to meaningful data narratives to drive company decisions and strategy
  • Track record of shipping high-quality machine learning products in production and at scale
  • Ability to thrive in a fast-paced, constantly improving, start-up environment that focuses on solving problems with iterative technical solutions

Nice-to-Haves
  • PhD in Math, Economics, Bioinformatics, Statistics, Engineering, Computer Science, or other quantitative fields
  • Strong perspective on data science engineering development cycle (data modeling, version control, documentation + testing, best practices for codebase development)
  • Familiarity with data orchestration platforms (Airflow, Dagster, Prefect)
  • Experience at a high-growth startup
  • Experience leveraging AI/LLMs for development or for internal workflows

Benefits available to all full-time Ramp employees (Global)
  • Flexible PTO
  • Centralized home-office equipment ordering
  • Health and wellness stipend
  • Budget for intra-office travel
  • Weekly coffee stipend

United States
  • 100% medical, dental & vision insurance coverage for you, with partial coverage for dependents
  • One Medical annual membership
  • 401(k), including employer match on contributions made while employed by Ramp
  • Fertility HRA (up to $10,000 per year)
  • Parental leave: up to 16 weeks (birthing + bonding) or 8 weeks (bonding only) at 100% pay
  • Pet insurance
  • In-office perks: lunch, snacks, drinks, and more
  • Relocation support to NYC or SF (as needed)
Canada
  • Group medical, dental, and vision coverage through Sun Life
  • Life, AD&D, and disability coverage
  • Fertility drug coverage (up to $4,000 lifetime)
  • Group Retirement Plan with employer match (RRSP + DPSP)
  • Parental leave: up to 16 weeks (birthing + bonding) or 8 weeks (bonding only) at 100% pay, with additional time available at reduced pay
  • Employee Assistance Program and virtual care through Lumino Health

United Kingdom
  • Private medical insurance through Freedom Elite
  • Virtual GP and at-home care via eMed x Livi
  • Workplace pension through Penfold, with salary sacrifice option
  • Parental leave: up to 16 weeks (birthing + bonding) or 8 weeks (bonding only) at 100% pay with additional time available at reduced pay

Referral Instructions
If you are being referred for the role, please contact that person to apply on your behalf.
Other notices
Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.
Beware of recruiting scams: Ramp will only contact you through official @Ramp.com email addresses and will never ask for payment or sensitive personal information during the hiring process.
Ramp Applicant Privacy Notice

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