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- Tasks: Join a team to design and build ML infrastructure for a leading quant firm.
- Company: A top-tier quant finance firm investing heavily in technology and innovation.
- Benefits: Enjoy competitive salary, generous bonuses, 25 days holiday, and monthly WFH allowance.
- Why this job: Work on impactful projects with autonomy and tackle complex challenges in ML Ops.
- Qualifications: Significant ML Ops experience and strong Python coding skills required.
- Other info: Opportunity to influence greenfield infrastructure in a collaborative environment.
The predicted salary is between 43200 - 72000 £ per year.
We’re looking for an experienced ML Ops engineer to join a newly formed team in a leading quant firm, responsible for ML Operations across a next-generation research platform. This is a high-impact, greenfield role where you’ll help design and build the future of ML infrastructure—from how data is shared, to how models are trained, deployed, and supported in production. ML is central to the firm’s trading strategies, and the platform you help shape will directly empower researchers and drive real business outcomes. Expect high autonomy and deep technical challenges—off-the-shelf tools won’t cut it, so you’ll often build bespoke solutions to handle complex interdependencies in ML workflows.
The Role: As part of the ML Workflows team, you’ll take ownership of building a mature, scalable ML research and deployment pipeline. You’ll work across the full ML lifecycle, including:
- Ingesting and managing new datasets
- Building tools for distributed training and inference
- Creating robust deployment and production support systems
You’ll leverage your ML Ops experience to assess the current landscape, identify gaps, and lead the technical direction of the new platform. Projects you’ll work on include:
- Implementing best-practice feature and model stores
- Proper versioning of features, data, and models
- Improving inference compute utilisation through smarter serving
- Building CI/CD pipelines for ML workflows
- Solving complex job orchestration for model training
- Developing tooling for robust validation, monitoring, and recovery in production
We’re looking for engineers who thrive on complex, open-ended challenges and want to set new standards for ML infrastructure.
You should have:
- Significant experience in ML Ops
- Strong coding skills in Python
- A deep understanding of ML lifecycle pain points and practical solutions
- Experience building systems for scaling training, versioning, and deployment
- Bonus points for experience with distributed compute, data engineering, and orchestration frameworks (e.g. Airflow, Ray, KubeFlow).
Why join? Top-tier quant finance firm with huge tech investment. Competitive base salary + 50–100%+ annual bonus. 25 days holiday, monthly WFH allowance, and £20/day lunch budget. Brand new, world-class offices in central London. Surrounded by some of the sharpest minds in engineering and research. If you’re ready to have real influence, work on greenfield infrastructure, and shape the ML future in a top business—we’d love to hear from you.
Senior Software Engineer - MLOps employer: Vertex Search
Contact Detail:
Vertex Search Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior Software Engineer - MLOps
✨Tip Number 1
Familiarise yourself with the latest trends and technologies in ML Ops. Being well-versed in tools like Airflow, Ray, and KubeFlow can give you an edge, as these are mentioned in the job description. Show your enthusiasm for continuous learning and how you can apply this knowledge to the role.
✨Tip Number 2
Network with professionals in the ML Ops field, especially those who work in quant finance. Attend relevant meetups or webinars to connect with potential colleagues and learn about their experiences. This can provide insights into the company culture and expectations, which you can leverage during interviews.
✨Tip Number 3
Prepare to discuss specific projects where you've tackled complex ML lifecycle challenges. Be ready to explain your thought process and the solutions you implemented. This will demonstrate your hands-on experience and problem-solving skills, which are crucial for the role.
✨Tip Number 4
Research the firm’s current ML infrastructure and any recent developments in their technology stack. Understanding their existing systems will allow you to propose innovative ideas during your interview, showcasing your proactive approach and genuine interest in contributing to their success.
We think you need these skills to ace Senior Software Engineer - MLOps
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience in ML Ops, particularly focusing on your coding skills in Python and any relevant projects you've worked on. Use specific examples that demonstrate your understanding of the ML lifecycle and how you've solved complex challenges.
Craft a Compelling Cover Letter: In your cover letter, express your enthusiasm for the role and the company. Discuss why you're excited about the opportunity to work on greenfield infrastructure and how your background aligns with the responsibilities outlined in the job description.
Showcase Relevant Projects: If you have experience with distributed compute, data engineering, or orchestration frameworks like Airflow or KubeFlow, be sure to mention these in your application. Highlight specific projects where you've implemented best practices in ML workflows.
Proofread Your Application: Before submitting, carefully proofread your application materials. Look for any spelling or grammatical errors, and ensure that all information is clear and concise. A polished application reflects your attention to detail and professionalism.
How to prepare for a job interview at Vertex Search
✨Showcase Your ML Ops Experience
Be prepared to discuss your previous experience in ML Ops in detail. Highlight specific projects where you built or improved ML infrastructure, focusing on the challenges you faced and how you overcame them.
✨Demonstrate Your Coding Skills
Since strong coding skills in Python are essential for this role, be ready to showcase your coding abilities. You might be asked to solve a problem or write a small piece of code during the interview, so practice common algorithms and data structures beforehand.
✨Understand the ML Lifecycle
Familiarise yourself with the entire ML lifecycle, from data ingestion to model deployment. Be prepared to discuss pain points you've encountered and practical solutions you've implemented in past roles.
✨Prepare for Technical Challenges
Expect to face complex, open-ended technical questions during the interview. Think about how you would approach building bespoke solutions for ML workflows and be ready to explain your thought process clearly.
