Machine Learning Engineer Language
Machine Learning Engineer Language

Machine Learning Engineer Language

Wandsworth Full-Time 48000 - 84000 ÂŁ / year (est.) No home office possible
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At a Glance

  • Tasks: Join our AI team to design and deploy cutting-edge machine learning systems.
  • Company: Enable is a leading SaaS company focused on AI-powered insights and performance monitoring.
  • Benefits: Enjoy ample paid time off, private health insurance, and a lucrative bonus plan.
  • Why this job: Be at the forefront of AI innovation while collaborating with top professionals in the field.
  • Qualifications: 5+ years in machine learning engineering; strong Python skills and experience with LLMs required.
  • Other info: We encourage women and underrepresented groups to apply, fostering an inclusive workplace.

The predicted salary is between 48000 - 84000 ÂŁ per year.

We help companies to increase profitability and simplify the complex with accurate, AI-powered insights, real-time performance monitoring, agreement optimization, and simplified rebate management. We’re hiring a Senior Machine Learning Engineer to join our AI and Architecture team, contributing to the design, development, and deployment of cutting-edge machine learning systems. In this role, you’ll work closely with ML scientists, data engineers, and product teams to help bring innovative solutions—such as retrieval-augmented generation (RAG) systems, multi-agent architectures , and AI agent workflows —into production. As a Senior Machine Learning Engineer, you’ll play a key role in developing and integrating cutting-edge AI solutions—including LLMs and AI agents —into our products and operations at a leading SaaS company. You’ll collaborate closely with product and engineering teams to deliver innovative, high-impact systems that push the boundaries of AI in rebate management. Design, build, and deploy RAG systems , including multi-agent and AI agent-based architectures for production use cases. Contribute to model development processes including fine-tuning, parameter-efficient training (e.g., Build evaluation pipelines to benchmark LLM performance and continuously monitor production accuracy and relevance. Work across the ML stack—from data preparation and model training to serving and observability—either independently or in collaboration with other specialists. Collaborate with MLOps, DevOps, and data engineering teams to ensure reliable model deployment and system integration. Stay informed on current research and emerging tools in LLMs, generative AI, and autonomous agents , and evaluate their practical applicability. 5+ years of experience in machine learning engineering, applied AI, or related fields. ~ Bachelor’s or Master’s degree in Computer Science, Machine Learning, Engineering , or a related technical discipline. ~ Strong foundation in machine learning and data science fundamentals —including supervised/unsupervised learning, evaluation metrics, data preprocessing, and feature engineering. ~ Proven experience building and deploying RAG systems and/or LLM-powered applications in production environments. ~ Proficiency in Python and ML libraries such as PyTorch, Hugging Face Transformers , or TensorFlow. ~ Hands-on experience with fine-tuning and distillation of large language models. ~ Experience with monitoring and maintaining ML systems in production, using tools like MLflow, Weights & Biases, or similar. ~ Strong communication skills and ability to work across disciplines with ML scientists, engineers, and stakeholders. PhD in Computer Science, Machine Learning, Engineering , or a related technical discipline. Experience with multi-agent RAG systems or AI agents coordinating workflows for advanced information retrieval. Familiarity with prompt engineering and building evaluation pipelines for generative models. Exposure to Snowflake or similar cloud data platforms. Broader data science experience, including forecasting, recommendation systems, or optimization models. Experience with streaming data pipelines , real-time inference , and distributed ML infrastructure. Contributions to open-source ML projects or research in applied AI/LLMs. Certifications in Azure, AWS, or GCP related to ML or data engineering. Once hired this person will have the job title Senior Machine Learning Engineer At Enable, we’re committed to your professional development and growth. Salary/TCC is just one component of Enable’s total rewards package. Paid Time Off: Ample days off + 8 bank holidays Private Health Insurance: Health and life coverage for you and your family Electric Vehicle Scheme: Lucrative Bonus Plan: Enjoy a rewarding bonus structure subject to company or individual performance Benefit from our equity program with additional options tied to tenure and performance Career Growth: Explore new opportunities with our internal mobility program Training: Access a range of workshops and courses designed to boost your professional growth and take your career to new heights According to LinkedIn\’s Gender Insights Report, women apply for 20% fewer jobs than men, despite similar job search behaviors. At Enable, we’re committed to closing this gap by encouraging women and underrepresented groups to apply, even if they don’t meet all qualifications. Enable is an equal opportunity employer, fostering an inclusive, accessible workplace that values diversity.

Machine Learning Engineer Language employer: Menlo Ventures

At Enable, we pride ourselves on being an exceptional employer, offering a dynamic work culture that fosters innovation and collaboration. Our commitment to employee growth is evident through our extensive training programmes, internal mobility opportunities, and a rewarding benefits package that includes ample paid time off, private health insurance, and a lucrative bonus structure. Join us in a vibrant location where your contributions to cutting-edge AI solutions will be valued and recognised, making a meaningful impact in the world of rebate management.
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Contact Detail:

Menlo Ventures Recruiting Team

StudySmarter Expert Advice 🤫

We think this is how you could land Machine Learning Engineer Language

✨Tip Number 1

Familiarise yourself with the latest advancements in machine learning, particularly in areas like retrieval-augmented generation (RAG) systems and large language models (LLMs). This knowledge will not only help you during interviews but also demonstrate your passion for the field.

✨Tip Number 2

Engage with the machine learning community by contributing to open-source projects or participating in relevant forums. This can enhance your visibility and showcase your practical skills, making you a more attractive candidate.

✨Tip Number 3

Network with professionals in the industry, especially those working in AI and machine learning roles. Attend meetups, webinars, or conferences to build connections that could lead to referrals or insider information about job openings.

✨Tip Number 4

Prepare to discuss your hands-on experience with ML libraries like PyTorch and TensorFlow. Be ready to share specific examples of projects where you've built and deployed machine learning systems, as this will highlight your practical expertise.

We think you need these skills to ace Machine Learning Engineer Language

Machine Learning Engineering
Data Science Fundamentals
Supervised/Unsupervised Learning
Model Evaluation Metrics
Data Preprocessing
Feature Engineering
Python Programming
PyTorch
Hugging Face Transformers
TensorFlow
Fine-tuning Large Language Models
Distillation of LLMs
Monitoring ML Systems
MLflow
Weights & Biases
Collaboration with Cross-Disciplinary Teams
Multi-Agent RAG Systems
AI Agent Workflows
Prompt Engineering
Building Evaluation Pipelines
Cloud Data Platforms (e.g., Snowflake)
Streaming Data Pipelines
Real-Time Inference
Distributed ML Infrastructure
Open-Source ML Contributions
Certifications in Azure, AWS, or GCP

Some tips for your application 🫡

Tailor Your CV: Make sure your CV highlights relevant experience in machine learning engineering, particularly with RAG systems and LLMs. Use specific examples to demonstrate your skills in Python and ML libraries like PyTorch or TensorFlow.

Craft a Compelling Cover Letter: In your cover letter, express your enthusiasm for the role and the company. Mention how your background aligns with their focus on AI-powered insights and your experience in collaborating with cross-functional teams.

Showcase Your Projects: If you have worked on any open-source ML projects or have contributions in applied AI, be sure to include these in your application. Highlight any experience with monitoring ML systems in production and tools like MLflow.

Highlight Continuous Learning: Mention any recent courses, certifications, or workshops you've completed related to machine learning or data engineering. This shows your commitment to professional development and staying updated with industry trends.

How to prepare for a job interview at Menlo Ventures

✨Showcase Your Technical Skills

Be prepared to discuss your experience with machine learning frameworks like PyTorch or TensorFlow. Highlight specific projects where you've built and deployed RAG systems or LLM-powered applications, as this will demonstrate your hands-on expertise.

✨Understand the Company’s AI Vision

Research the company's approach to AI and how they integrate machine learning into their products. Being able to articulate how your skills align with their goals will show that you're genuinely interested in the role and the company.

✨Prepare for Collaborative Scenarios

Since the role involves working closely with ML scientists, data engineers, and product teams, be ready to discuss your experience in cross-functional collaboration. Share examples of how you've successfully worked with diverse teams to deliver impactful solutions.

✨Stay Updated on Industry Trends

Familiarise yourself with the latest research and tools in LLMs and generative AI. Being able to discuss current trends and their practical applications will demonstrate your commitment to staying at the forefront of the field.

Machine Learning Engineer Language
Menlo Ventures
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