Senior Machine Learning EngineerMavenoid • London, England, United Kingdom
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Senior Machine Learning Engineer
Mavenoid
- London, England, United Kingdom
- London, England, United Kingdom
À propos
Senior Machine Learning Engineer
and shape the next product features to help people around the world improve support for their hardware devices. You will help process large volumes of textual conversations, search queries, and documents (over 1M text conversations per month) and assess new LLM and NLP models to build and improve ML features in our products.
Technical Stack
Python, NLP/ML libraries (langchain, langfuse, huggingface, PyTorch, etc.)
LLM providers (OpenAI, Anthropic, Google, Mistral) and hosted models
Docker on GCP cloud services
Way of Working
Small team with shared responsibilities
Focus on shipping to production and seeing usage
Keep up with ML developments and balance speed and code quality
You Will
Work fully remote and meet in person a few times a year
Own specific features from scoping to production delivery
Evaluate ideas and propose metrics to explore/implement/ship new things
Contribute to ML models, features, service architecture, and platform at scale
Qualifications
ML engineer who cares about product and user outcomes
At least 4 years of industry experience in ML/data‑science, specifically NLP/generative and with conversational data
Experience with ML problem‑solving, diagnosing errors, and hypothesising next steps
Experience shipping ML services using Docker, GCP services (Cloud Run, Vertex), and CI/CD practices
Experience with real‑time LLM services for RAG conversational systems in production
Voice or agentic system experience is a plus
Experience working in a compact ML team with shared ownership
Responsibilities
Scope, build, and deliver ML features to production
Think ahead for long‑term ML development in the product
Follow software and ML engineering best practices to keep things humming
Day‑to‑Day At The Individual Level
40% exploring/developing ML/NLP problems
10% ensuring ML features solve the right problem with the right assumptions with the product team
30% shipping for production and keeping live features
20% free exploration/investigation for long term
Onboarding Timeline
First month: complete remote onboarding, meet teams, familiarize with platform, ramp up codebase, focus on one feature to evaluate metrics and propose next steps.
Three months: work on one feature improvement, collaborate on architecture and product, take over a service and push the envelope, tackle new features from data exploration to feasibility and concept assessment.
Six months: propose and implement first large platform or architecture change, become familiar with CI/CD/evaluation pipeline, own part of the platform, identify improvement areas.
Seniority Level Mid‑Senior level
Employment Type Full‑time
Job Function Engineering and Information Technology
Industries Software Development
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Compétences linguistiques
- English
Avis aux utilisateurs
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