At a Glance
- Tasks: Develop and optimise AI-driven vision systems for real-time flock monitoring.
- Company: Join The Healthy Chicken Company, an innovative ag-tech start-up transforming the poultry industry.
- Benefits: Enjoy a salary up to £60k, share options, hybrid working, and 25 days holiday.
- Why this job: Be part of a diverse team making a positive impact on animal welfare and the environment.
- Qualifications: 4+ years in Computer Vision, strong Python skills, and experience with ML frameworks required.
- Other info: Work in a fun, inclusive culture with exposure to cutting-edge technologies.
The predicted salary is between 42000 - 84000 £ per year.
We’re The Healthy Chicken Company, an ag-tech start-up that believes you can have your chicken and eat it too! Using advanced AI, we’re on a mission to transform the poultry industry by improving the lives of the 1.8 trillion chickens reared over the next few decades. Our system ‘watches’ flocks and sheds with smart cameras and sensors and delivers data and insights to farmers (and others in the supply chain) that help improve welfare. Our team is made up of industry-leading technical talent and ambitious entrepreneurs who genuinely want to make a difference. We embrace diversity, representing 10+ nationalities and people from all walks of life. We’re fun, quirky, humble, ambitious, and passionate. It’s an exciting time of growth for FLOX – and we’re looking for like-minded people to join the team.
We are looking for a Computer Vision Engineer with a strong software engineering mindset to develop and optimise AI-driven vision systems. This role seats in the Tech Team, and will report to the Engineering Manager / Head of Data and it will play a key role in building production-ready models, scaling AI infrastructure, and advancing our core vision capabilities. This is an exciting opportunity to push the boundaries of AI in real-world applications, working with state-of-the-art Computer Vision, Deep Learning, and MLOps technologies. Occasionally, and in line with your strengths and interests, you may be asked to work outside your job description.
Our tech stack
- Languages: Python (PyTorch, NumPy, OpenCV)
- ML Frameworks: PyTorch, TensorFlow, OpenCV
- Infrastructure: AWS, GCP, Docker, Kubernetes, MLFlow
- MLOps Tools: DVC, Weights & Biases, TensorRT
- Version Control & CI/CD: GitHub, GitLab, Jenkins
Key Responsibilities
- Develop & optimise high-performance Computer Vision and deep learning algorithms for real-time flock monitoring.
- Implement scalable AI solutions that transition seamlessly from research to production-level software.
- Own the full AI pipeline, including data collection, labeling, processing, and model deployment.
- Advance core vision features, such as visual weighing, behavior tracking, and health assessment of chickens.
- Optimise model efficiency & inference speed for deployment on edge devices, and cloud-based systems.
- Collaborate with engineers & researchers to enhance model accuracy, robustness, and interpretability.
- Participate in code reviews, debugging, and validation/testing to ensure high-quality, maintainable code.
- Stay ahead of industry trends in AI/ML, deep learning architectures, and MLOps best practices.
About You
- 4+ years of hands-on experience developing Computer Vision and Deep Learning models in production environments.
- Previous experience working with real-time video.
- Strong software engineering skills, including clean coding, modular design, and best practices.
- Experience deploying ML models at scale, with knowledge of MLOps, model optimisation, and inference acceleration.
- Proficiency in Python and AI/ML frameworks like PyTorch & TensorFlow.
- Familiarity with cloud platforms (AWS, GCP, Azure) and containerised environments (Docker, Kubernetes).
- Knowledge of camera models and calibration.
- Ability to communicate complex AI concepts clearly to both technical and non-technical stakeholders.
- A natural collaborator that is keen on knowledge sharing and supporting other team members.
- Can travel to our E8 London HQ ~ twice a week.
- Start-up / scale-up experience a bonus.
Compensation, Perks & Benefits
- Up to £60k p.a. depending on experience and location.
- Share options package of £20k.
- Hybrid flexible working.
- 25 days’ holidays (excluding bank holidays).
- Lunch and snacks provided in the office.
- Inclusive and relaxed company culture: we welcome everyone, we encourage you to be yourself and dress as you like.
- Exposure to state-of-the-art technologies.
- A young and international work environment.
- A chance to work with well-respected experts, including AI and robotics.
We are committed to equality of opportunity for all staff and applications from all individuals are encouraged regardless of age, socioeconomic background, disability, sex, gender reassignment, sexual orientation, pregnancy and maternity, race, religion or belief and marriage and civil partnerships. We strongly encourage applications from womxn and queer folk.
Seniority level: Mid-Senior level
Employment type: Full-time
Job function: Other
Industries: IT Services and IT Consulting
Computer Vision Engineer employer: Flox
Contact Detail:
Flox Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Computer Vision Engineer
✨Tip Number 1
Familiarise yourself with the specific technologies mentioned in the job description, such as Python, PyTorch, and TensorFlow. Having hands-on experience with these tools will not only boost your confidence but also demonstrate your readiness to hit the ground running.
✨Tip Number 2
Showcase any previous projects or experiences that involved real-time video processing or computer vision applications. Be prepared to discuss these in detail during interviews, as they can highlight your practical skills and problem-solving abilities.
✨Tip Number 3
Network with professionals in the AI and tech community, especially those who work in ag-tech or similar fields. Engaging with industry experts can provide valuable insights and potentially lead to referrals, which can significantly enhance your chances of landing the job.
✨Tip Number 4
Stay updated on the latest trends in AI, deep learning, and MLOps. Being knowledgeable about current advancements will not only prepare you for technical discussions but also show your passion for the field and commitment to continuous learning.
We think you need these skills to ace Computer Vision Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience in Computer Vision and Deep Learning. Focus on specific projects where you've developed algorithms or worked with real-time video, as these are key aspects of the role.
Craft a Compelling Cover Letter: In your cover letter, express your passion for AI and how it can transform industries like poultry farming. Mention your familiarity with the tech stack they use, such as Python, PyTorch, and AWS, to show you’re a great fit.
Showcase Your Projects: Include links to any relevant projects or GitHub repositories that demonstrate your skills in developing and optimising AI-driven vision systems. This will give them a clear view of your capabilities.
Highlight Collaboration Skills: Since the role involves working with engineers and researchers, emphasise your ability to communicate complex AI concepts clearly. Provide examples of past collaborations that led to successful outcomes.
How to prepare for a job interview at Flox
✨Showcase Your Technical Skills
Be prepared to discuss your experience with Python, PyTorch, and TensorFlow. Bring examples of projects where you've developed Computer Vision or Deep Learning models, especially those that were deployed in production environments.
✨Understand the Company's Mission
Familiarise yourself with The Healthy Chicken Company's goals and how they use AI to improve poultry welfare. This will help you align your answers with their mission and demonstrate your genuine interest in their work.
✨Prepare for Problem-Solving Questions
Expect technical questions that assess your problem-solving abilities. Practice explaining your thought process when tackling challenges related to model optimisation, inference speed, and real-time video processing.
✨Emphasise Collaboration and Communication
Highlight your ability to work in teams and communicate complex concepts clearly. Share examples of how you've collaborated with engineers and researchers to enhance model accuracy and robustness.