Senior Machine Learning Engineer

Overview

Join Mavenoid as a 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.

• Python, NLP/ML libraries (langchain, langfuse, huggingface, PyTorch, etc.)
• LLM providers (OpenAI, Anthropic, Google, Mistral) and hosted models
• Docker on GCP cloud services

• Small team with shared responsibilities
• Focus on shipping to production and seeing usage
• Keep up with ML developments and balance speed and code quality

• 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

• 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

• 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

• 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

• 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.

Mid‑Senior level

Full‑time

Engineering and Information Technology

Software Development