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- Tasks: Develop cutting-edge algorithms for advanced weapon systems and engage in project lifecycles.
- Company: Join a leading firm innovating in intelligent autonomous systems and guided weapon technology.
- Benefits: Enjoy flexible working options, competitive salary, and opportunities for professional growth.
- Why this job: Be part of a dynamic team shaping the future of technology with real-world impact.
- Qualifications: Degree or PhD in a relevant field with strong programming and mathematical skills required.
- Other info: Experience with tools like Matlab, Python, and machine learning frameworks is a plus.
The predicted salary is between 43200 - 72000 £ per year.
The opportunity: Our algorithms are central to the design of sophisticated guided weapon systems products. These algorithms are developed throughout the lifecycle of the product and include research studies for future developments.
Intelligent Autonomous Systems (IAS) Engineers are involved in project lifecycles, playing a pivotal role in our product development, including:
- Technical development of specific algorithms or studies for key programmes, including feasibility studies, algorithm design and trade-off studies, trial preparations, analysis and reporting, architecture definition, validation of algorithms and models.
- Technical assessments and investigations into various issues, developing solutions either independently or as part of a team.
- Engaging with algorithm users to understand and respond to their needs, ensuring algorithms are fit for purpose.
What we’re looking for from you:
- Degree/PhD in a related field or a degree with mathematical content and programming skills.
- Relevant experience (post-doctoral or industrial) in robotics, data fusion, tracking/estimation, pattern discovery & recognition, statistical inference, optimisation, and machine/deep learning algorithms, including real-time implementation and validation & verification.
- Experience with Matlab, Simulink, Stateflow, Python, PyTorch, TensorFlow, OpenAI-Gym/Universe, or Model-Based Design is desirable.
Engineers are encouraged to develop broad and in-depth knowledge across various fields, with specific knowledge or experience in the following areas being beneficial:
- Robotics, guidance, and autonomous decision-making: routing, motion/trajectory planning, optimisation, guidance and control, decision theory, MDPs/POMDPs, game theory, decision support, multi-agent systems.
- Data fusion and state estimation/tracking algorithms: Kalman Filtering, multi-model tracking, particle filters, grid-based estimation, multi-sensor fusion, data association, Bayesian networks, Dempster-Shafer theory.
- Machine Learning: regression, pattern recognition, Gaussian processes, latent variable methods, support vector machines, neural networks, Bayesian inference, random forests, anomaly detection, clustering.
- Deep Learning: reinforcement learning, Monte Carlo tree search, deep regression/classification, embeddings, recurrent networks, NLP.
- Computer Vision algorithms: structure from motion, navigation, SLAM, pose estimation.
Algorithm Engineer employer: COPELLO GLOBAL LTD
Contact Detail:
COPELLO GLOBAL LTD Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Algorithm Engineer
✨Tip Number 1
Familiarise yourself with the specific algorithms and technologies mentioned in the job description. Brush up on your knowledge of Kalman Filtering, reinforcement learning, and computer vision algorithms, as these are crucial for the role.
✨Tip Number 2
Engage with online communities or forums related to algorithm engineering and machine learning. Networking with professionals in the field can provide insights into current trends and may even lead to referrals.
✨Tip Number 3
Consider working on personal projects that showcase your skills in algorithm design and implementation. Having a portfolio of relevant projects can set you apart during the interview process.
✨Tip Number 4
Prepare for technical interviews by practising problem-solving questions related to algorithms and data structures. Being able to demonstrate your thought process and technical skills will be key in impressing the interviewers.
We think you need these skills to ace Algorithm Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience in algorithm development, robotics, and machine learning. Use specific examples that demonstrate your skills in Matlab, Python, and any other tools mentioned in the job description.
Craft a Strong Cover Letter: In your cover letter, express your passion for algorithm engineering and how your background aligns with the company's needs. Mention specific projects or experiences that relate to the responsibilities outlined in the job description.
Showcase Technical Skills: Clearly list your technical skills related to the job, such as experience with Kalman Filtering, deep learning frameworks, or computer vision algorithms. Provide context for how you've applied these skills in previous roles or projects.
Highlight Collaborative Experience: Since the role involves engaging with algorithm users and working in teams, include examples of past collaborations. Describe how you’ve worked with others to develop solutions or improve algorithms, showcasing your teamwork and communication skills.
How to prepare for a job interview at COPELLO GLOBAL LTD
✨Showcase Your Technical Skills
Make sure to highlight your experience with relevant programming languages and tools like Python, Matlab, and TensorFlow. Be prepared to discuss specific projects where you've applied these skills, especially in areas like machine learning or robotics.
✨Understand the Product Lifecycle
Familiarise yourself with the entire lifecycle of algorithm development for guided weapon systems. Be ready to discuss how you can contribute at each stage, from feasibility studies to validation and reporting.
✨Engage with Real-World Applications
Demonstrate your understanding of how algorithms are used in practical scenarios. Discuss any experience you have with autonomous systems or decision-making processes, and be prepared to explain how you would approach real-world challenges.
✨Prepare for Technical Questions
Expect in-depth technical questions related to algorithms, data fusion, and machine learning. Brush up on key concepts like Kalman Filtering and reinforcement learning, and be ready to solve problems on the spot or explain your thought process clearly.
