At a Glance
- Tasks: Design and deploy machine learning models to optimise messaging across various channels.
- Company: Join a forward-thinking tech company focused on AI and innovation.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Other info: Collaborative environment with a focus on experimentation and cross-functional teamwork.
- Why this job: Make a real impact by shaping the future of AI-assisted development.
- Qualifications: Experience in machine learning, PyTorch, and optimisation problems required.
The predicted salary is between 70000 - 90000 € per year.
Requirements
- You have strong experience building and deploying machine learning models in production environments at scale.
- You are comfortable translating business problems into ML solutions and discussing trade-offs with cross-functional partners.
- You have worked on complex optimization problems such as ranking systems or multi-objective decision-making.
- You bring hands-on experience with PyTorch and distributed systems such as Ray or similar frameworks.
- You understand experimentation deeply and can design reliable tests in environments with interacting metrics.
- You are able to analyze results using approaches like causal inference or metric decomposition when needed.
- You have experience with or curiosity about reinforcement learning and long-term optimization systems.
- You enjoy working across disciplines and navigating ambiguity while shaping strategy and direction.
What the job involves
- Design, build, and ship machine learning models that optimize messaging across push, email, and in-app channels.
- Plan and run A/B experiments in a multi-objective environment, balancing conversion, engagement, retention, and reachability.
- Contribute to reinforcement learning systems that optimize for long-term user outcomes rather than immediate interactions.
- Partner with product managers, data scientists, and engineers to define what success looks like and how to measure it.
- Own the full ML lifecycle, from data and modeling to deployment, monitoring, and iteration.
- Integrate ML models with upstream systems, including domain value signals and opportunity generation frameworks.
- Help shape the future of AI-assisted development within the team, exploring how tools can accelerate experimentation and delivery.
Senior Machine Learning Engineer (Messaging Platform) in London employer: Deepstreamtech
As a Senior Machine Learning Engineer at our innovative company, you will thrive in a dynamic work culture that values collaboration and creativity. We offer competitive benefits, including professional development opportunities and a supportive environment that encourages growth and exploration of cutting-edge technologies. Located in a vibrant tech hub, our team is dedicated to shaping the future of AI-assisted development while ensuring meaningful contributions to user engagement and retention.
StudySmarter Expert Advice🤫
We think this is how you could land Senior Machine Learning Engineer (Messaging Platform) in London
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect with people on LinkedIn. You never know who might have the inside scoop on job openings or can refer you directly.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your machine learning projects, especially those involving PyTorch or complex optimisation problems. This will give potential employers a taste of what you can do.
✨Tip Number 3
Prepare for interviews by brushing up on your problem-solving skills. Be ready to discuss how you've tackled real-world business problems with ML solutions and how you measure success in your projects.
✨Tip Number 4
Don't forget to apply through our website! We love seeing candidates who are genuinely interested in joining us at StudySmarter. Tailor your application to highlight your experience with reinforcement learning and A/B testing.
We think you need these skills to ace Senior Machine Learning Engineer (Messaging Platform) in London
Some tips for your application 🫡
Tailor Your CV:Make sure your CV highlights your experience with machine learning models and any relevant projects. We want to see how you've tackled complex problems, so don’t hold back on the details!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain how your skills align with our needs, especially around optimization and experimentation. Let us know why you’re excited about the role and what you can bring to the team.
Showcase Your Technical Skills:Don’t forget to mention your hands-on experience with tools like PyTorch and any distributed systems you’ve worked with. We love seeing practical examples of your work, so feel free to include links to projects or GitHub repos!
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it’s super easy!
How to prepare for a job interview at Deepstreamtech
✨Know Your ML Models Inside Out
Make sure you can discuss your experience with building and deploying machine learning models in production. Be ready to explain the trade-offs you've navigated and how you've translated business problems into effective ML solutions.
✨Showcase Your Experimentation Skills
Prepare to talk about your experience with A/B testing and designing reliable experiments. Highlight any complex optimization problems you've tackled, especially in multi-objective environments, and be ready to discuss how you measure success.
✨Familiarise Yourself with Relevant Frameworks
Brush up on your knowledge of PyTorch and distributed systems like Ray. Be prepared to discuss how you've used these tools in past projects and how they can help optimise messaging across various channels.
✨Emphasise Collaboration and Strategy
Demonstrate your ability to work across disciplines by sharing examples of how you've partnered with product managers, data scientists, and engineers. Discuss how you've shaped strategy and direction in ambiguous situations, focusing on long-term user outcomes.
