At a Glance
- Tasks: Design and implement scalable data pipelines and custom ML algorithms for real-time applications.
- Company: Join StackAdapt, a leading tech company revolutionising digital marketing with AI.
- Benefits: Enjoy competitive salary, health benefits, remote work, and generous paid time off.
- Other info: Diverse and inclusive culture with excellent career growth opportunities.
- Why this job: Make an impact in a collaborative environment while tackling complex machine learning challenges.
- Qualifications: Strong understanding of algorithms, software design, and experience with ML implementations.
StackAdapt is the leading technology company that empowers marketers to reach, engage, and convert audiences with precision. With 465 billion automated optimizations per second, the AI-powered StackAdapt Marketing Platform seamlessly connects brand and performance marketing to drive measurable results across the entire customer journey. The most forward-thinking marketers choose StackAdapt to orchestrate high-impact campaigns across programmatic advertising and marketing channels.
We're looking to add a Machine Learning Engineer to our Data Science team! This team works on solving complex problems for StackAdapt's digital advertising platform. You'll be working directly with our Data Scientists, Machine Learning Engineers, Engineering teams, and our CTO/Co-Founder on building pipelines and ad optimization models. With databases that process millions of requests per second, there's no shortage of data and problems to tackle. StackAdapt is a remote-first company with teams around the world. Our teams work in a fully distributed environment, and we are open to candidates based in the UK, Ireland or Germany. This role will remain open until February 27, 2026. Applications will be reviewed on a rolling basis, and the posting will close once the deadline is reached.
What you'll be doing:
- Design modular and scalable real time data pipelines to handle huge datasets
- Understand and implement custom ML algorithms in a low latency environment
- Work on microservice architectures that run training, inference, and monitoring on thousands of ML models concurrently
What you'll bring to the table:
- Have the ability to take an ambiguously defined task, and break it down into actionable steps
- Have deep understanding of algorithm and software design, concurrency, and data structures
- Experience in implementing probabilistic or machine learning algorithms
- Interest in designing scalable distributed systems
- A high GPA from a well-respected Computer Science program
- Enjoy working in a friendly, collaborative environment with others
StackAdapt's Enjoy:
- Highly competitive salary
- Retirement/ 401K/ Pension Savings globally
- Competitive Paid time off packages including birthday's off!
- Access to a comprehensive mental health care program
- Health benefits from day one of employment
- Work from home reimbursements
- Optional global WeWork membership for those who want a change from their home office and hubs in London and Toronto
- Robust training and onboarding program
- Coverage and support of personal development initiatives (conferences, courses, books etc)
- Access to StackAdapt programmatic courses and certifications to support continuous learning
- An awesome parental leave program
- A friendly, welcoming, and supportive culture
- Our social and team events!
StackAdapt is a diverse and inclusive team of collaborative, hardworking individuals trying to make a dent in the universe. No matter who you are, where you are from, who you love, follow in faith, disability (or superpower) status, ethnicity, or the gender you identify with (if you’re comfortable, let us know your pronouns), you are welcome at StackAdapt. If you have any requests or requirements to support you throughout any part of the interview process, please let our Talent team know.
We use artificial intelligence (AI) to streamline the resume reviews of candidates and assess their fit based on the criteria outlined in the job posting. We do not use AI to make any final hiring or interview decisions.
About StackAdapt: We've been recognized for our diverse and supportive workplace, high performing campaigns, award-winning customer service, and innovation.
Remote Machine Learning Engineer (Remote) in Middlesbrough employer: StackAdapt
StackAdapt is an exceptional employer that fosters a remote-first culture, allowing you to collaborate with a diverse team of innovative professionals from the comfort of your home. With competitive salaries, comprehensive health benefits from day one, and robust support for personal development, including access to training and certifications, StackAdapt prioritises employee growth and well-being. Join us in a friendly, inclusive environment where your contributions directly impact the future of digital advertising.
StudySmarter Expert Advice🤫
We think this is how you could land Remote Machine Learning Engineer (Remote) in Middlesbrough
✨Tip Number 1
Network like a pro! Reach out to current employees at StackAdapt on LinkedIn or other platforms. Ask them about their experiences and any tips they might have for the interview process. It’s all about making connections!
✨Tip Number 2
Prepare for technical interviews by brushing up on your machine learning algorithms and data structures. Practice coding challenges on platforms like LeetCode or HackerRank. We want to see you shine in those problem-solving skills!
✨Tip Number 3
Showcase your projects! If you've worked on any relevant machine learning projects, make sure to highlight them during your interview. Discuss the challenges you faced and how you overcame them. This is your chance to impress us with your hands-on experience!
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen. Plus, it shows you’re genuinely interested in joining our awesome team at StackAdapt. Let’s get you on board!
We think you need these skills to ace Remote Machine Learning Engineer (Remote) in Middlesbrough
Some tips for your application 🫡
Tailor Your CV:Make sure your CV is tailored to the Machine Learning Engineer role. Highlight relevant experience, especially with ML algorithms and data pipelines. We want to see how your skills align with what we do at StackAdapt!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you're excited about the role and how you can contribute to our Data Science team. Let us know what makes you a great fit for StackAdapt!
Showcase Your Projects:If you've worked on any cool projects related to machine learning or data processing, make sure to mention them. We love seeing practical applications of your skills, so don’t hold back!
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way to ensure your application gets into the right hands. Plus, it shows us you’re serious about joining our awesome team!
How to prepare for a job interview at StackAdapt
✨Know Your Algorithms
Brush up on your machine learning algorithms and be ready to discuss how you've implemented them in past projects. StackAdapt values a deep understanding of these concepts, so prepare to explain your thought process and the outcomes of your implementations.
✨Showcase Your Problem-Solving Skills
Be prepared to tackle hypothetical scenarios during the interview. Think about how you would break down complex tasks into actionable steps, as this is crucial for the role. Practising with real-world problems can help you articulate your approach clearly.
✨Familiarise Yourself with Data Pipelines
Since you'll be designing scalable data pipelines, it’s essential to understand the principles behind them. Be ready to discuss your experience with modular architectures and how you’ve handled large datasets in previous roles.
✨Emphasise Collaboration
StackAdapt thrives on teamwork, so highlight your experiences working in collaborative environments. Share examples of how you’ve successfully worked with cross-functional teams, especially with data scientists and engineers, to achieve common goals.
