Master's Thesis Opportunity - Spring of 2027

Neo4j is the

graph intelligence platform that transforms data into knowledge to power the next generation of intelligent applications and AI

systems. It includes enterprise-ready knowledge graphs for accurate, explainable, and governed AI; the most comprehensive,

trusted, and easy-to-deploy graph capabilities across any environment and data source; and an unmatched ecosystem trusted by 84 of

the Fortune 100 and supported by the world’s largest graph community. Intelligence that works. Results that matter.

Built to work everywhere and integrate with everything across every cloud for dynamic, personalized, and autonomous AI systems.

We deliver quicker results, contextual knowledge, and solutions that impact customers and employees across the business.

As business, society and knowledge become increasingly connected, our technology promotes innovation by helping

organizations to find and understand data relationships. We created, drive and lead the graph database category, and we’re

disrupting how organizations leverage their data to innovate and stay competitive.

Are you

at the end of your studies and want to immerse yourself in graph technology? We are now looking for students who want to do their

Master’s Thesis alongside us at Neo4j!

As part of Neo4j engineering in Malmö, you will work with a diverse team of talented

colleagues worldwide. You will receive advice and continuous support from us - we are experts in graph technology and positioned

to help you perform to the best of your ability.

Past Thesis Topics:​

​Force Directed Drawing Algorithms and Parameter

Optimisation:​

Through my thesis I have implemented and compared some different graph drawing algorithms in

addition to some methods to speed up the slow parts of these algorithms. These algorithms were then used to test what to the

best of my knowledge is a novel approach to select parameter values for graph drawing algorithms. For this, I use

methods similar to those used in Machine Learning to select parameter values and measure the utility of any set of parameters

by creating my own utility function. I created this function by looking at objective measures of drawing quality that

are commonly known, such as the number of edge crossings, along with the time it took to draw a given graph. The

resulting method for parameter optimisation could find significant increases in the speed of graph drawing for several

of my implemented drawing algorithms without compromising drawing quality. Furthermore, the approach is not specific to

any parameter set, and can with some modification be applied to any graph drawing algorithm dependent on some

constants.

​Modeling Profiling Data in a Graph Database for Performance

Analysis:​

Benchmarking is an important part of the development process for any mission-critical application. By

inspecting profiling data, developers can identify bottlenecks and performance regressions before they reach the

customers.

Neo4j runs an extensive benchmarking suite on

its database, resulting in a huge collection of profiling data collected each week. These profiles are commonly visualized

individually as flame graphs which are inspected manually. Finding patterns and differences among multiple profiles is difficult

to do manually, due to the size and complexity of the data. We propose a framework for identifying bottlenecks and regressions by

modeling the profiling data as call-stack trees in a graph database. We demonstrate the usefulness of the framework for

cross-profile analysis such as time series analysis and aggregation-based methods. We conclude that there is much potential in

this approach and our thesis can be used as a decision basis for organizations wanting to implement a similar framework.

Using

a graph database to model profiling data has many advantages and is suitable for the tree-like structure of the data. It makes the

data more accessible and facilitates flexible querying in which the user can ask questions about the data and perform non-trivial

aggregation. It has already aided Neo4j in the process of pinpointing the cause of some performance issues. The main disadvantage

is the complexity involved in importing large quantities of data.

Navigating Failures in Distributed Systems: A

Comparative Study of Failure Detection Algorithms:​

Failure detection algorithms are used to identify unhealthy nodes

in distributed systems. The goal of this study was to improve Neo4j’s use of failure detection algorithms by exploring two paths:

either optimising their existing Lighthouse algorithm or by implementing a new algorithm. Existing algorithms were surveyed and

the SWIM algorithm was implemented. A baseline was established and evaluated against parameter-optimized versions of SWIM and

Lighthouse in a simulated network. The results show that Baseline is scalable and reliable but slow, Lighthouse is fast but less

accurate, and SWIM is moderately fast and the least accurate but generates the least network load. In conclusion, the chosen

parameters of a failure detector are to a great extent more important than the algorithm itself. Furthermore, to successfully

optimise parameters it is crucial to have a scalable simulator and precise system requirements to manage the trade-off between

speed, accuracy, and network load.

Cache replacement policies and their impact on graph database

operations:​

In this master thesis project, the page caching strategy of the Neo4j database is researched and attempted

to be improved. Focusing on the eviction protocol of the page cache, several different algorithms are evaluated in both

experimental prototyping using Python, and in the Neo4j database kernel. Using the measurements of the prototypes and the results

of the Neo4j benchmarks conclude that the current page replacement policy is hard to beat with a different

strategy. However, modifying the current page replacement policy by using a global instead of thread-local data structure and

tuning parameters increased the hit rate and throughput. Furthermore, the measurements on the different implementations

showed that the hit rate can be increased at the cost of some overhead, but implementing a complicated algorithm quickly

increases the overhead and might decrease the throughput enough to make the algorithm ineffective.

Randomly

generating execution plans for bug detection in Neo4j:​

In recent years, Graph Database Management Systems(GDBMS) has

increased in popularity for many use cases. One of the most popular GDBMS is Neo4j, which uses Cypher as a query language. With

the increasing use of GDBMS in many business-critical applications, the need to test Neo4j and its competitors has become

critical. One common practice for identifying bugs in a database system is using randomly generated tests, known as fuzz testing.

Previously, this has been done by randomly generating queries, and several tools are currently available for this purpose. When

executing a Cypher query, the query goes through several processing steps to ensure a correct result returns quickly. One of the

intermediate structures used in the query processing is the execution plan, which details how the runtime should solve the

query. In this thesis, we propose a novel approach to fuzz testing GDBMS by randomly generating execution plans. Our tool

utilizes differential testing between different Neo4j runtimes, which allows for identifying incorrect results returned from

one or more of the runtimes. These types of bugs are known as logic bugs. We can also identify situations when the Neo4j

runtimes throw unexpected exceptions. The testing suite identified 20 bugs within the Neo4j, of which 11 were logic

bugs. This approach to fuzz testing has proven helpful in identifying errors within the Neo4j runtimes, which previously received

insufficient coverage by fuzz testing using queries. Other database management systems that utilize execution plans can

benefit from the approach proposed by this thesis. The main drawbacks of this new approach are that it is not easily

portable between different GDBMS and requires access to the query processing source code.

​

We tackle challenges in:​

Concurrency and parallelism

Distributed systems and fault tolerance

Language design and type systems

Performance tuning and benchmarking

Cloud

architecture and service design

Site Reliability Engineering and

cloud automation

Continuous Integration and Continuous Delivery

Does this topic sound

interesting? Please apply!​

If you are applying as a group, please apply separately

and indicate who you are applying together with in your Cover Letter.

Project

start​

Jan/Feb 2027

Why Join Neo4j?​

Neo4j is,

without question, the most popular graph intelligence platform in the world. We have customers in every industry globally, and our

products are a proven product/market fit. Joining our team is an opportunity to shape the future of data and analytics. Below are

just a few exciting facts about Neo4j.

• Neo4j is one of the fastest-scaling technology companies in this

industry. It recently surpassed $200M in annual

recurring revenue (ARR), doubling its ARR over the past three

years.

• Raised the biggest funding round in database history ($325M Series F). Backed by world-class investors

like Eurazeo, GV (formerly Google Ventures), and Inovia Capital, Neo4j has raised over $600M in funding and is currently valued at

over $2Bn. This puts Neo4j among the most well-funded database companies in history.

• 84% of the Fortune 100 and 58% of

the Fortune 500 use Neo4j. Examples include Boston Scientific, BT Group, Caterpillar, Cisco, Comcast, Department for Education UK, eBay, NBC News, Novo Nordisk, Worldline, and others.

• Co-founder and CEO Emil Eifrem has built an amazing culture that

prides itself on relationships, inclusiveness, innovation, and customer success.

• Countless industry awards. Massive enterprises and individual developers/data scientists love Neo4j. A

strong sense of community and ecosystem is built around the platform.

• A recent Forrester Total Economic Impact™ Study

cited Neo4j as delivering 417% ROI to customers.

Research

shows that members of underrepresented communities are less likely to apply for jobs when they don’t meet all the qualifications.

If this is part of the reason you hesitate to apply, we’d encourage you to reconsider and give us the opportunity to

review your application. ​At Neo4j, we are committed to building awareness and helping to improve these issues.

One of our central objectives is to provide an inclusive, diverse, and equitable workplace for everyone to develop their

potential and have a positive, career-defining experience. We look forward to receiving your application.

Neo4j is a Silicon

Valley company with a Swedish soul. We foster collaboration and each of us is empowered to contribute and put our innovative stamp

on projects. We hire candidates who reflect the following Neo4j core values:

(we)-[:VALUE]->(relationships)

(we)-[:FOCUS_ON]->(userSuccess)

(we)-[:THRIVE_IN]->(:Culture {type: [‘Open’,

‘Inclusive’]})

(we)-[:ASSUME]->(:Intent {direction:’Positive’})

(we)-[:WELCOME]->(:Discussions {nature: ‘IntellectuallyHonest’})

(we)-[:DELIVER_ON]->(ourCommitments)

Neo4j is committed to protecting

and respecting your privacy. Please read the privacy notice regarding Neo4j's recruitment

process to understand how we will handle the personal data that you

provide.

More information at www.neo4j.com.

©2026 Neo4j, Inc., Neo Technology®, Neo4j®, Cypher®, Neo4j Bloom™, Neo4j Graph Data Science Library™,

Neo4j® Aura™, and Neo4j® AuraDB™ are registered trademarks or a trademark of Neo4j, Inc. All other marks are owned by their

respective companies.