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- Tasks: Design and develop machine learning models to solve real-world business challenges.
- Company: Join an award-winning AI and Big Data company leading innovation globally.
- Benefits: Remote work, competitive salary, and opportunities for professional growth.
- Why this job: Make a significant impact in AI while collaborating with talented professionals.
- Qualifications: 5+ years in data analysis or data science; degree in a quantitative field.
- Other info: Be part of a trailblazing team shaping the future of AI and cloud technology.
The predicted salary is between 48000 - 72000 £ per year.
About Quantiphi: Quantiphi is an award-winning Applied AI and Big Data software and services company, driven by a deep desire to solve transformational problems at the heart of businesses. Our signature approach combines groundbreaking machine-learning research with disciplined cloud and data-engineering practices to create breakthrough impact at unprecedented speed. Quantiphi has seen 2.5x growth YoY since its inception in 2013. Headquartered in Boston, with 4,000+ professionals across the globe, Quantiphi leverages Applied AI technologies across multiple industry verticals (Telco, BFSI, HCLS etc.) and is an established Elite/Premier Partner of NVIDIA, Google Cloud, AWS, Snowflake, and others.
We have been recognized with:
- 17x Google Cloud Partner of the Year awards in the last 8 years
- 3x AWS AI/ML award wins
- 3x NVIDIA Partner of the Year titles
- 2x Snowflake Partner of the Year awards
- Recognized Leaders by Gartner, Forrester, IDC, ISG, Everest Group and other leading analyst and independent research firms
We offer first-in-class industry solutions across Healthcare, Financial Services, Consumer Goods, Manufacturing, and more, powered by cutting-edge Generative AI and Agentic AI accelerators. We have been certified as a Great Place to Work for the third year in a row - 2021, 2022, 2023.
Be part of a trailblazing team that’s shaping the future of AI, ML, and cloud innovation. Your next big opportunity starts here!
Role: Sr Machine Learning Engineer
Experience Level: 5+ years
Employment type: Full Time
Location: Remote (UK)
Job Summary: We are seeking a Sr Machine Learning Engineer to join our growing team. In this role, you will design, develop, evaluate, and deploy traditional machine learning models and solutions to solve real-world business problems. You’ll work closely with cross-functional teams including Data Science, Software Engineering, and Product to translate analytical insights into scalable production systems. This is an exciting opportunity for a data-savvy individual with a strong business acumen to make a significant impact on our customer retention and long-term success.
Key Responsibilities:
- Design, train, validate, and optimize classical ML models (e.g., regression, decision trees, random forests, gradient boosting) for structured and semi-structured data.
- Perform feature engineering, model selection, hyperparameter tuning, and evaluation using tools like scikit-learn, XGBoost, LightGBM.
- Build robust data preprocessing pipelines and scalable workflows for model training and inference.
- Collaborate on the development of agentic AI components — systems capable of autonomously planning, adapting, and executing tasks toward high-level goals with limited human oversight.
- Integrate classical machine learning models into agentic AI workflows where predictive capabilities inform planning, decision-making, and action selection.
- Develop and evaluate interfaces between agentic components and external tools, APIs, or systems to enable real-world actions.
- Work closely with data engineers, software developers, product owners, and domain experts to translate analytical insights into operational workflows.
- Document model development, deployment decisions, and agentic AI design choices.
- Contribute to best practices in ML lifecycle management and agentic system governance.
- Experience with other GCP services like Cloud Storage, Dataflow, or Vertex AI is a plus.
Required Skills & Qualifications:
- Bachelor's or Master's degree in a quantitative field such as Data Science, Statistics, Mathematics, Computer Science, Economics, or a related discipline.
- 5 years of progressive experience in data analysis, business intelligence, or data science roles.
What is in it for you:
- Make an impact at one of the world’s fastest-growing AI-first digital engineering companies.
- Upskill and discover your potential as you solve complex challenges in cutting-edge areas of technology alongside passionate, talented colleagues.
- Work where innovation happens - work with disruptive innovators in a research-focused organization with 60+ patents filed across various disciplines.
- Stay ahead of the curve—immerse yourself in breakthrough AI, ML, data, and cloud technologies and gain exposure working with Fortune 500 companies.
Senior Machine Learning Engineer employer: Quantiphi
Contact Detail:
Quantiphi Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior Machine Learning Engineer
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect with current employees at Quantiphi. A personal touch can make all the difference when it comes to landing that interview.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your machine learning projects. Whether it's a GitHub repo or a personal website, having tangible examples of your work can really impress hiring managers.
✨Tip Number 3
Prepare for the technical interview! Brush up on your ML concepts and be ready to discuss your past experiences. Practice coding challenges and be prepared to explain your thought process clearly.
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, you’ll be one step closer to joining a team that’s shaping the future of AI and ML.
We think you need these skills to ace Senior Machine Learning Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to the Senior Machine Learning Engineer role. Highlight relevant experience and skills that align with the job description, especially in machine learning models and data engineering.
Craft a Compelling Cover Letter: Your cover letter should tell us why you're the perfect fit for Quantiphi. Share your passion for AI and ML, and how your background can contribute to our mission of solving transformational problems.
Showcase Your Projects: Include specific projects or achievements that demonstrate your expertise in machine learning and data analysis. We love seeing real-world 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 for us to receive your application and ensures you’re considered for this exciting opportunity at Quantiphi.
How to prepare for a job interview at Quantiphi
✨Know Your ML Models Inside Out
Make sure you can discuss classical machine learning models like regression, decision trees, and gradient boosting in detail. Be prepared to explain how you've used these models in past projects, including the challenges you faced and how you overcame them.
✨Showcase Your Collaboration Skills
Since this role involves working closely with cross-functional teams, be ready to share examples of how you've successfully collaborated with data engineers, software developers, and product owners. Highlight any specific projects where teamwork led to successful outcomes.
✨Demonstrate Your Business Acumen
Quantiphi values candidates who understand the business impact of their work. Prepare to discuss how your machine learning solutions have contributed to customer retention or other business goals in previous roles. This will show that you can translate technical insights into real-world applications.
✨Familiarise Yourself with Their Tech Stack
Research Quantiphi's use of tools like scikit-learn, XGBoost, and GCP services. If you have experience with these technologies, be ready to discuss it. If not, consider brushing up on them so you can speak confidently about how you would approach integrating ML models into agentic AI workflows.
