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 will

• Complete 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 will

• Work 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 will

• Propose, 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.