Senior ML Engineer

Senior ML Engineer

Full-Time 56700 - 69300 £ / year (est.) On-site
Academic Key

At a Glance

  • Tasks: Design and implement scalable ML services, leading MLOps capabilities and collaborating with data scientists.
  • Company: Join Anaplan, a leader in AI-infused business decision-making with a vibrant culture.
  • Benefits: Competitive salary, inclusive environment, and opportunities for personal and professional growth.
  • Other info: Embrace diversity and innovation in a dynamic team focused on excellence and collaboration.
  • Why this job: Make an impact by optimising forecasting solutions for top global companies like Coca-Cola and LinkedIn.
  • Qualifications: Strong backend development experience, proficiency in Python, and familiarity with cloud services.

The predicted salary is between 56700 - 69300 £ per year.

At Anaplan, we are a team of innovators focused on optimizing business decision-making through our leading AI-infused scenario planning and analysis platform so our customers can outpace their competition and the market. Our customers rank among the who's who in the Fortune 50. Coca-Cola, LinkedIn, Adobe, LVMH and Bayer are just a few of the 2,400+ global companies who rely on our best-in-class platform.

Our Winning Culture is the engine that drives our teams of innovators. We champion diversity of thought and ideas, we behave like leaders regardless of title, we are committed to achieving ambitious goals, and we love celebrating our wins - big and small. Supported by operating principles of being strategy-led, values-based and disciplined in execution, you'll be inspired, connected, developed and rewarded here. Everything that makes you unique is welcome; join us and let's build what's next - together!

You will join the Predictive Intelligence engineering team within Anaplan, building the backend services that power the ML Engine behind the Syrup platform and Anaplan's forecasting solutions. The team is responsible for the production execution of forecasting and predictive models, the MLOps infrastructure supporting our data scientists, and the data processing services that deliver insights to enterprise customers. This role reports to the Director of Engineering for Predictive Intelligence and works closely with data scientists, ML engineers, and platform partners.

Your Impact

  • Design and implement scalable, fault-tolerant predictive intelligence services and features as a core contributor to the ML Engine backend.
  • Lead the technical implementation of MLOps capabilities, including model training pipelines, deployment workflows, and runtime infrastructure for production ML.
  • Partner with data scientists to productionize models and graduate experimental work into stable, observable production services.
  • Drive the evolution of forecasting, scoring, and data processing services with a focus on performance, scalability, and cost efficiency.
  • Take part in on-call rotations and own the operational health of high-availability production services, including incident response and post-incident improvements.
  • Lead design reviews and code reviews, raising the quality bar across the team and mentoring mid-level and junior engineers.
  • Identify and drive cross-cutting platform improvements that benefit multiple services and teams.

Your Qualifications

  • Strong professional software engineering experience building production backend services.
  • Strong proficiency in Python, with a track record of writing performant, well-tested production code.
  • Hands-on experience operating containerized services on Kubernetes in at least one major cloud (AWS, GCP, or Azure).
  • Experience with data warehousing or analytics technologies such as Snowflake, Iceberg, Trino, or Postgres.
  • Experience designing, deploying, and operating ML models in production, including familiarity with MLOps tooling such as MLflow.
  • Demonstrated ability to work autonomously, take ownership of meaningful systems, and deliver against ambiguous requirements.
  • Track record of being on-call for production services and contributing to operational excellence.
  • Bachelor's degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience.

Preferred Skills

  • Experience with gradient-boosted tree models, neural networks, and optimization solvers in production.
  • Familiarity with data orchestration tools (e.g., Prefect, Airflow, dbt) and modern data lake architectures.
  • Experience working closely with data scientists to operationalize research code.
  • Multi-cloud experience across AWS, GCP, and Azure.
  • Background in forecasting, demand planning, or retail/supply chain domains.

Our Commitment to Diversity, Equity, Inclusion and Belonging (DEIB)

We believe attracting and retaining the best talent and fostering an inclusive culture strengthens our business. DEIB improves our workforce, enhances trust with our partners and customers, and drives business success. Build your career in a place where diversity, equity, inclusion and belonging aren't just words on paper - this is what drives our innovation, it's how we connect, and it contributes to what makes us a market leader.

We believe in a hiring and working environment where all people are respected and valued, regardless of gender identity or expression, sexual orientation, religion, ethnicity, age, neurodiversity, disability status, citizenship, or any other aspect which makes people unique. We hire you for who you are, and we want you to bring your authentic self to work every day! We will ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, perform essential job functions.

Senior ML Engineer employer: Academic Key

Deliveroo is an exceptional employer that prioritises the well-being of its employees while fostering a culture of innovation and collaboration. With a commitment to diversity and inclusion, we offer competitive benefits tailored to each country, including generous leave allowances and support for charitable causes. As part of a rapidly growing global team, you'll have the opportunity to work on impactful projects that shape the future of customer support, all while enjoying a dynamic and supportive work environment.

Academic Key

Contact Details:

Academic Key Recruitment Team

StudySmarter Expert Advice🤫

We think this is how you could land Senior ML Engineer

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We think you need these skills to ace Senior ML Engineer

Python
MLOps
Kubernetes
AWS
GCP
Azure
Data Warehousing

Some tips for your application 🫡

Show off your coding skills:When applying for a software engineering role, it's super important to showcase your coding skills. Make sure your CV includes your tech stack, any relevant programming languages you’re comfortable with, and examples of projects you've worked on. If you have a GitHub profile, link it up! We love to see code in action.

Tailor your portfolio:For a full-time role, we’d expect to see some solid examples of your work in your portfolio. Make sure to include at least two or three projects that highlight your problem-solving skills and your ability to work with different technologies. Focus on the projects that are most relevant to the position at Academic Key.

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How to prepare for a job interview at Academic Key

✨Brush Up on Your Coding Skills

For a full-time software engineering role, it's crucial that we stay sharp with our coding abilities. Expect technical questions that might involve solving problems on the spot or discussing algorithms. Practise on platforms like LeetCode or HackerRank to get comfortable with the types of questions that often come up.

✨Know Your Tools and Frameworks

Make sure we’re well-acquainted with the tools and technologies listed in the job description. Familiarise ourselves with any specific frameworks or programming languages mentioned. If Academic Key uses React or Node.js, for instance, be ready to discuss how we’ve used them in previous projects or coursework.

✨Showcase Your Projects

Bring along a portfolio that highlights our best work. This could be code samples, GitHub repositories, or any side projects we’ve built. Make sure we can talk through our thought process for each project, especially the challenges we faced and how we solved them—this shows our problem-solving skills in action.

✨Prepare for Behavioural Questions

While technical skills are key, full-time positions also require cultural fit. Be ready to discuss our previous experiences and how we handle teamwork, conflict, and deadlines. Brush up on the STAR method—Situation, Task, Action, Result—to clearly articulate our past experiences when discussing how we've contributed to a team.