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Applied Research Engineer Jobs (NOW HIRING)

Hebbia

Applied Research Engineer, Agents

New York, NY · On-site

The Role As an Applied Research Engineer, you will be the bridge between research, industry, and application shaping the future of our core natural language processing systems. You will be ...

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Applied Research Engineer information

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How much do applied research engineer jobs pay per year?

As of Sep 10, 2026, the average yearly pay for applied research engineer in the United States is $106,012.00, according to ZipRecruiter salary data. Most workers in this role earn between $104,000.00 and $104,000.00 per year, depending on experience, location, and employer.

What is an applied research engineer?

Applied research engineers are professionals who bridge the gap between theoretical research and practical applications. They take scientific discoveries and innovative concepts from research and develop real-world solutions, often creating prototypes, refining processes, or improving technologies. Their work can span various industries, including technology, manufacturing, and healthcare, and they frequently collaborate with both researchers and product development teams. Applied research engineers play a crucial role in turning ideas into tangible products or services that address specific needs.

How does an applied research engineer typically collaborate with product development teams to bring research solutions into production?

Applied Research Engineers often work closely with cross-functional product development teams to translate experimental models into scalable and robust solutions. This involves frequent meetings to align research objectives with business needs, adapting prototypes for real-world constraints, and providing technical guidance to ensure seamless integration. Effective communication and documentation are essential, as is the ability to iterate quickly based on feedback from software engineers, data scientists, and product managers. This collaborative environment helps bridge the gap between cutting-edge research and practical, user-facing applications.

What are the key skills and qualifications needed to thrive as an applied research engineer, and why are they important?

To thrive as an Applied Research Engineer, you need a strong background in mathematics, computer science, and engineering principles, usually evidenced by an advanced degree in a related field. Familiarity with programming languages (such as Python or C++), machine learning frameworks, and experience with simulation or data analysis tools are typically required. Critical thinking, creativity, and excellent problem-solving skills set standout candidates apart, along with the ability to clearly communicate complex findings. These skills ensure the effective development and implementation of innovative solutions that bridge theoretical research and practical engineering applications.

What is the difference between Applied Research Engineer vs Data Scientist?

AspectApplied Research EngineerData Scientist
Required CredentialsBachelor's or Master's in Engineering, Computer Science, or related fields; often requires experience in research or developmentBachelor's or Master's in Data Science, Statistics, or related fields; often requires strong analytical and programming skills
Work EnvironmentResearch labs, R&D departments, technology companies focusing on product developmentBusiness environments, analytics teams, consulting firms analyzing data for insights
Employer & Industry UsageTech companies, research institutions, manufacturing firmsFinance, healthcare, marketing, tech industries

Applied Research Engineers focus on developing new technologies and prototypes through research, often working in R&D settings. Data Scientists analyze and interpret complex data to inform business decisions. While both roles require technical skills and programming knowledge, Applied Research Engineers are more involved in creating innovative solutions, whereas Data Scientists focus on data analysis and modeling.

Do I need a PhD to be an applied research engineer?

A PhD is not strictly required to become an applied research engineer, but it is often preferred for roles involving advanced research, development, and innovation. Many employers value relevant experience, strong technical skills, and proficiency with tools like machine learning frameworks and programming languages. Educational requirements vary depending on the company and project complexity.
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Applied Research Engineer jobs near you
Infographic showing various Applied Research Engineer job openings in the United States as of September 2026, with employment types broken down into 1% Internship, 1% As Needed, 88% Full Time, 9% Part Time, and 1% Contract. Highlights an 79% Physical, 3% Hybrid, and 18% Remote job distribution, with an average salary of $106,012 per year, or $51 per hour.

Founding Applied Research Engineer

San Francisco, CA • On-site

Rox
See more jobs at Roxexternal

Full-time

Re-posted 14 days ago

Key responsibilities

  • Design and run research programs tied directly to cost-efficient inference, signal classification, recommendation, and personalization problems.

  • Build evaluation frameworks that measure trajectory quality and translate research findings into infrastructure with measurable production impact.

  • Work on agent memory, retrieval, and context systems alongside engineering teams.

Job description

Why This Role Exists
Foundation models are commoditizing. Defensibility comes from specialized models, proprietary training signals, and evaluation ownership. Every applied AI company we benchmark against like Decagon, Harvey, Sierra, Cursor has already moved. The window to claim frontier applied AI for revenue is closing in the next few months.
Rox is in market. We run agents against enterprise data at scale, every day. We see exactly where research meets production and where the data is dirty, state is changing, and being wrong costs (a lot of) money.
The Applied Research team exists to close that gap permanently.
What This Team Works On
Four problems we care about right now:
Cost-efficient inference for Clever Columns. Distill a Rox-trained model from frontier teachers so per-account enrichment runs at 1/20th the cost without quality loss. Ships first. Doesn't require trajectory attribution.
Signal classification across the public knowledge graph. A small, fast classifier that distinguishes genuine buying signals from noise across the news, jobs, and filings corpus we already ingest at scale. Powers Recommended Next Moves and Auto Prospecting. Cleanest data subset.
Personalization grounding and hallucination detection. A reward model that catches fabricated prospect context in Sequences in real time. This is the most underrated production failure mode in outbound AI. Trained on cross-customer consensus edits.
Sequencing policy under sparse, delayed rewards. Offline-to-online RL on multi-touch trajectories with intermediate signals as proxies for terminal outcomes. Long-horizon flagship. Hard. [Depends on trajectory instrumentation in progress with Platform Eng.]
These are not benchmark problems. They have real SLAs and real customers depending on them.
What You'll Do
  • Design and run research programs tied directly to the four above.
  • Build evaluation frameworks that measure trajectory quality, not just final output, because most eval infrastructure measures end results and we care about the path.
  • Work on agent memory, retrieval, and context systems alongside elite and competitive engineering minds.
  • Translate findings into infrastructure with measurable production impact. Help define where Rox Research goes next.
What We're Looking For
You have spent real time thinking about how agents fail in practice, not just on benchmarks. You have built evaluation systems and know exactly where standard approaches break down. You can write code well enough to implement your own ideas, run your own experiments, and ship things that make it into production.
You move fast. The environment changes monthly and the team ships continuously.
Particularly relevant: agent evaluation and behavioral benchmarking; retrieval-augmented generation and knowledge graph systems; RL applied to real-world agent behavior; production ML systems (latency, reliability, observability); post-training and model adaptation for production use cases.
A PhD is not required. Strong research instincts and the ability to ship are.
What Success Looks Like
First few weeks: you understand Rox's architecture, where the production problems are, and where the research gaps are. You have opinions and you share them.
First few months: you are running experiments that directly inform how we build. Something you worked on is in production.
Over time: you are defining the research agenda for the most interesting applied AI problem in the enterprise. The systems you build are things no one else has built before, because no one else has the structural data position to build them.
Why Join Now
We are at an unusual moment. Large enough to have real scale, real customers, and genuinely interesting research problems. Small enough that you are one of a handful of people shaping what the Applied Research function looks like and what it prioritizes.
The team is extraordinary: IMO, IOI, and ICPC medalists, researchers from DeepMind and OpenAI. The feedback loop is a live enterprise system, not a leaderboard. If that's not more interesting to you than publishing for the sake of publishing, this probably isn't the right fit.
San Francisco, onsite. We relocate exceptional people.

Apply