Machine Learning Engineer (Hybrid)
Janes, United Kingdom
Job Description
Janes enables militaries, governments, and defence companies to make critical decisions. Our expert-driven tradecraft, developed over 120 years, combined with human-machine teaming, delivers assured open-source intelligence across military capabilities and order of battle, equipment, events, countries, companies, and markets.
Linking millions of assured data points, Janes data model creates a framework of interconnected open source defence intelligence. This allows our customers to integrate all relevant data and connections into a single intelligence environment to deliver a more complete and accurate answer. Using Janes, our customers can use their scarce resource more effectively, to get to better decisions with higher confidence, more quickly.
As a Machine Learning Engineer, you will play a crucial role in the development and implementation of cutting-edge artificial intelligence products.
Your responsibilities will involve designing and constructing sophisticated machine learning models, as well as refining and updating existing systems.
In order to thrive in this position, you must possess exceptional skills in statistics and programming, as well as a deep understanding of data science and software engineering principles.
Your ultimate objective will be to create highly efficient self-learning applications that can adapt and evolve over time, pushing the boundaries of AI technology. Join us and be at the forefront of innovation in the field of machine learning.
- Study and transform data science prototypes
- Design machine learning systems
- Research and implement appropriate ML algorithms and tools
- Develop machine learning applications according to requirements
- Select appropriate datasets and data representation methods
- Run machine learning tests and experiments
- Perform statistical analysis and fine-tuning using test results
- Train and retrain systems when necessary
- Extend existing ML libraries and frameworks
- Keep abreast of developments in the field
Requirements
- Proven experience as a Machine Learning Engineer or similar role
- Ability to write robust code in Python and preferably also in JavaScript
- Strong understanding of Natural Language Processing (NLP)
- Familiarity with machine learning frameworks (Pytorch, HuggingFace) and libraries (like scikit-learn)
- Experience in AWS or other similar cloud platforms
- Excellent communication skills
- Ability to work in a team
Desireble skills:
- Understanding of data structures, data modeling and software architecture
- Deep knowledge of math, probability, statistics and algorithms
- Understanding and ability to orchestrate resources in cloud (Terraform)
- Defining data augmentation pipelines
- Analyzing the errors of the model and designing strategies to overcome them
- Deploying models to production
- Outstanding analytical and problem-solving skills
- BSc in Computer Science, Mathematics or similar field; Master’s degree is a plus
We believe Janes is truly a great place to work. Our values and leadership code drive everything we do, and we understand that the right behaviours and culture will always result in the best outcomes for our customers, our colleagues, our shareholders, and our business. We provide a supportive, stretching, and dynamic environment with the ability for you to grow rapidly, both personally and professionally.
Janes is an inclusive and equal opportunities employer and encourages applications regardless of age, race, disability, religion / belief, sexual orientation, gender reassignment, marriage or civil partnership, pregnancy/maternity, or gender.
Although this role is advertised as full time, Janes believes that flexibility at work can provide many significant benefits both to our colleagues and the business. We already work in a hybrid style across all offices and regions and can support different ways of working and offer different flexible working arrangements. So, if you are interested and have any requirements or needs in the way you would like to work, please apply, and speak to us about this.
We will always consider part time or flexible applications.