Internships and student jobs – Spring 2023

Post by Manal Benmalek,
Employer Branding
April 7th, 2023

Gain hands-on experience with our tech internship and student job program at Railnova

We are pleased to announce that, following a season of successful job fairs (JUNIA, Polytech Lille, Forum ON Emploi) and the second edition of the Railnova Career Day, we are now sharing our latest openings for May onwards. We are looking for enthusiastic and motivated students to join our team in any of the exciting opportunities for tech internships in general that we offer!

Our internships provide hands-on experience and an opportunity to work alongside experienced professionals in the tech industry. As an intern at Railnova, you will be able to gain valuable skills and knowledge that will help you jump-start your career.

We are committed to supporting and extending our student team, and we welcome applications from individuals who are passionate about technology and eager to learn. So, don’t hesitate to join us on an exciting journey and jump on the Railnova train!

SupplyX : Create a Manufacturing Resources Planning module for a Django-based (Python, Database, Django)


The Supply Chain Team is responsible for the supply of raw materials, planning of (in-house) production, and delivery to our customers in a timely manner.

Railnova’s products are very complex, with hundreds of parts to be purchased on a regular basis. This work is made even more complex as lots of Electronic parts are hard to source or have extremely long lead times. It is thus crucial to have proper visibility on what needs to be purchased and to identify blocking parts as quickly as possible to avoid stopping the production.

The current tools (excel sheets and todos) are too limited to do proper planning of the purchasing, so we started the development of a small MRP.

This internship aims to improve and integrate this MRP into our in-house, Django-based ERP, SupplyX.

Duration: 2-3months


During this internship, your will need to push your analytical skills to model the interactions/dependencies/intricacies/interdependencies of purchase, stock, manufacturing, and shipping events using the Django framework, write an algorithm to solve them and see your work being deployed and used as an everyday tool of the Supply Chain Team.


Supply Chain for business logic, Hardware to take over the initial proof of concept developed, and Christian for technical guidance.

SupplyX : Create a Quality control module for a Django-based business software (Python, Postgres Database, Django)


The Industrialization Team is responsible for the realization of the quality control of the calling kits that are manufactured in-house.

Railnova’s products are very complex, and the quality control is highly important to deliver quality product to our customers. The current tools (excel sheets and todos) are too limited to increase the efficiency of the quality control and to record the information easily.

This internship aims to specify, develop, test and deploy a QC module with in our internal ERP that is based on Python and Django with a Postgres database.

Duration: 2-3months


During this internship, your will need to push your analytical skills to model the interactions/dependencies/intricacies/interdependencies of the current QC activities based on the bill of material of the cabling kits. You will suggest improvements of the current operations while keeping quality in mind and a zero-defect approach. You will write specifications and code the module using the Django framework. The module aims to drive, automate and ease quality control and related reporting.


Hardware Operations Christian for technical guidance.

Supervision: Christophe (and CSP for technicalities of SupplyX)


Be part of the supply chain team and discover what is usually hidden inside a company. The supply chain is overall responsible for purchasing, planning, provisioning, internal and external logistics and supporting the production team. Amongst other things, you will:

  • Execute incoming material control.
  • Pick in stock the requirements for the production of cable kits and railsters.
  • Maintain and improve a stock organization and enforce stock cleanliness.
  • Register stock moves in IT systems and physically move pieces to the required area.
  • Order consumables when the stock is low to avoid shortages.
  • Perform inventory control
  • Ship orders and RMA Returns to our customers
  • Maintain documentation

Duration: 3 months – it can be part-time or full

Start: June – Low priority and to be discussed based on the workload


  • Organised
  • Rigorous
  • Methodical
  • Google Suite (Sheets)
  • English reading and writing


  • Overall responsibility is to execute the operational tasks described above in the project description.

Project: Documentation clean-up and harmonisation

Duration: To be discussed


  • Used to Github
  • Used to Markdown
  • Structured
  • Methodical
  • Organized
  • Rigorous


Installation procedure documentation, schematics, and other documents clean-up. Definition of specific templates, and move to the new generic doc template format with the new numeration system.

Project: Documentation and tools

Duration: 3 weeks

Requirements: Student in Electrical/Electromechanical/Mechatronic engineering


Inventory of all the cabling kits per asset classes and variants. Creation of data requests for each cabling kit to check if the cabling kit matches new locos from different customers.

Project: Integration study of Railster UG2/2U inside trains

Duration: 4 months

Requirements: Master student in Electrical/ Electronic/ Electromechanical/Mechatronic engineering

Responsibilities: You will join the Train interface engineering team which is responsible for the electrical and mechanical integration of the Railster inside the assets, from the electrical analysis of the potential connection points to the production of the cabling kits that will be physically installed.

In that context, you will take part of Railster integration study projects with the goal of having the Railster UG2/2U safely installed and connected to the asset busses. You will work on the design of a cabling kit with cables and connectors solutions to integrate the Railster to new types of locomotives or on the feasibility study of upgrading the existing cabling kit using Railster UG1/Railster UG2 to Railster UG2U, which is another version of the Railster UG2 recently designed.

Example of tasks you will do:

  • analysing the train electrical schematics to identify connection points on the train
  • designing cabling schematics and easy-to-assemble connectors for field busses (MVB, Can Bus, J1708, RS484, Ethernet,  RS232, Ethernet, …) and sensors (battery, level sensors, temperature sensors, vibration sensors…)
  • participating in the design of mechanical installation in the train
  • writing up simple and pragmatic installation instructions so that our Clients can easily install the Railster and the cable kit themselves.
  •  produce cabling kits

You will be responsible for the end-to-end implementation of your project, communication, escalation of issues and decision making at your level.

Project: Production and industrialisation of cabling kits

Duration: To be agreed

Requirements: Bachelor student in Electrical/Electronic/Electromechanical/Mechatronic


You will be part of the team working on the production of cabling kits, the quality check, documentation or test benches.

Contribute to Infrastructure State of the Art implementation in Systems, Network, Log and Metrics matters

Project: Contribute to Infrastructure State of the Art implementation in Systems, Network, Log and Metrics matters

Duration: 2 to 4 months

Requirements: Linux, Python


You’ll be part of the infrastructure team managing the systems and networks underlying the whole business at Railnova. We are working with Terraform, Kubernetes, Helm, etc, and we are looking for 2 students that are willing to improve the quality of the infrastructure at Railnova.

Supervision/Coaching: Cédric & Nicolas

Locomotive matching with maps

Project: At Railnova, one of the most critical data we get from a locomotive is its position, coming mainly from a GNSS system (as GPS). Matching the GNSS position with the Railway map will improve the precision of the locomotive position and, consequently, data that depend on it (for example, km counters, which have a critical role in maintenance planning). The matching can be challenging in some dense areas of the Railway, like near in yard areas, so advanced methods will need to be tested.

Duration: 2 to 3 months

Requirements: good skills in Python

Responsibilities: Develop an algorithm that performs the matching, test its performance (accuracy and timing), deploy the algorithm in production (if the performances allow it and time remaining)

Automated optical inspection (AOI) to improve Railnova’s electronic production quality

Project: The operator currently performs quality control of the electronics produced at Railnova, which is a time-consuming and tedious task. The goal of this project would be to develop an algorithm that would detect the most impacting anomalies from pictures of the boards and deliver the result in a straightforward way to the person in charge of the production process.

Duration: 3 to 4 months

Requirement: good skills in Python

Responsibility: Development and training of an algorithm that detects the anomalies on the board pictures, develop a tool with a simple UI that provides the anomaly detected and allows to give feedback to the algorithm.

Battery charge gauge of Railnova’s IoT devices

Project: Railnova installs IoT boxes in trains (aka Railster) that collect data and send them in real-time to our infrastructure. Locomotives and trains can remain in parking for long periods, during which periodic data collection is still useful (for example, to monitor the train batteries). Having an estimation of the time on battery will be useful to anticipate a Railster being out of batteries and to communicate that information to our customers.

After several years with our devices on the field, we have a nice dataset of battery discharges collected. The goal of the internship would be to analyse this dataset and to develop a model from our data able to estimate the time of discharge remaining.

Duration: 1-2 months

Requirement: good skills in Python

Responsibility: Development and training of an algorithm that predict the time on batteries, the study of the explainability of the algorithm and making the result of the model available

Mining the Railnova incident ticket database

Project: Railnova has a database containing incident tickets filled by the locomotive drivers and the return of experience from the maintenance workshops. The goal of the internship will be to use some state-of-the-art NLP algorithms  to get value from these tickets:

by extracting from tickets content some keys features, like the failing component or the type of failure by predicting the impact of the failure on the locomotive exploitation (possibly by crossing with the telematic data from the locomotive)

by detecting poorly written tickets: unclear tickets or containing too many topics in one. That could be used to inform the ticket author of a possible problem and thus would help to increase the overall quality of the tickets

Duration: 2-3 months

Requirement: good skills in Python

Responsibility: Deployment and testing of some NLP algorithms to analyze tickets. Understand and make reports on the results and anticipate how such models could be deployed.

Apply now!

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