Job Description

The role
You will be part of the ML team at Mavenoid, shaping the next product features to help people around the world get better support for their hardware devices. The core of your work will be to understand users' questions and problems to fill the semantic gap.

The incoming data consists mostly of textual conversations, search queries and documents (more than 1M text conversations per month and growing volume on voice). You will help to process this data and assess new LLM and NLP models to build and improve the set of ML features in the products.

Tech stack
We work:in Python with NLP/ML libs, including langchain, langfuse, huggingface, pytorch (among others) major LLM providers (OpenAI, Anthropic, Google, Mistral) and hosted models deploying with docker on GCP cloud services


We are pragmatic on which tool to use for each approach, as long as it can be properly packaged for production.

Way of working
We are a small team -- by design -- and share responsibilities. We care about:shipping to production and see usage keeping up with the ML developments balance between speed and codebase quality


You will:work fully remote and meet IRL few times a year focus on specific features and own the process from scoping to production delivery evaluate ideas and propose the right metrics to explore/implement/ship new things contribute on ML models and features but also service architecture and the platform at scale

Qualifications

You are an ML engineer who cares about product and user outcomes At least 4 years of industry experience in ML/data-science roles, specifically in NLP/generative and with conversational data Experience with ML problem-solving, diagnosing errors and hypothetising next steps Experience with shipping ML services using Docker (build images, manage revisions), GCP services (cloud run, instances, vertex) and CI/CD practices Experience with real-time LLM services for RAG conversational systems in production Experience with voice or agentic system is a plus Experience with working in a compact ML team with shared responsibilities & ownership

Responsibilities

scope, build, and deliver ML features to production thinking ahead for long-term ML development in the product following software and ML engineering best practices to keep things humming

Day-to-day at the individual level:40% exploring/developing ML/NLP problem 10% making sure the ML features are solving the right problem with the right assumptions with product team 30% shipping for production and keeping live features 20% free exploration/investigation for long term


In your first month, you willComplete Mavenoid's remote onboarding program Meet with the ML/Product/CS teams to understand what is being worked on Familiarize yourself with our platform and product, and processes Ramp up the codebase with co-working sessions and/or time on your side Focus on one feature to understand the evaluation metrics and propose a step ahead in accuracy/efficiency/performance


In your 3 months, you willWork on one first feature improvement to go over explore/implement/evaluate/ship loop Collaborate with the rest of the team to bring your input on the system architecture and the product Take over one service and push the envelope Tackle one or more new feature, from data exploration to feasibility and concept assessment, in collaboration with the product lead.


In your 6 months, you willPropose, discuss, coordinate and implement your first large platform or architecture change Be familiar with a large portion of the platform, including the details of our CI/CD/evaluation pipeline, machine learning services, and integrations to external systems * Own a part of the platform and be able to identify areas of improvement.