Senior Machine Learning Engineer

About Mavenoid

Mavenoid is the Intelligent Support Platform for products and devices, delivering best-in-class customer support through intelligent troubleshooting and personalized remote assistance. Our product empowers manufacturers and sellers with genius-level support while significantly reducing operational costs. Having successfully raised our Series B funding, we are in an exciting phase of rapid growth, yet remain lean enough for every new team member to make a substantial impact. Although founded in Stockholm, Sweden, Mavenoid operates as a remote-first company, with team members distributed across Sweden, the United Kingdom, the United States, and beyond.

The Role: Senior Machine Learning Engineer

As a Senior Machine Learning Engineer, you will be an integral part of Mavenoid's ML team, actively shaping the next generation of product features designed to enhance support for hardware devices globally. Your core responsibility will involve understanding complex user questions and problems to bridge the semantic gap, ultimately improving user experience.

You will primarily work with a high volume of incoming data, predominantly textual conversations, search queries, and documents (exceeding 1 million text conversations per month, with growing voice data). Your expertise will be crucial in processing this data and evaluating advanced LLM and NLP models to develop and refine the machine learning features embedded within our products.

Tech Stack

• Python NLP/ML libraries, including langchain, langfuse, huggingface, and pytorch.
• Major LLM providers such as OpenAI, Anthropic, Google, and Mistral, alongside hosted models.
• Deployment strategies utilizing Docker on GCP cloud services.

We adopt a pragmatic approach to tool selection, prioritizing effectiveness and proper packaging for production.

Way of Working

Our ML team is intentionally compact, fostering shared responsibilities and a strong sense of ownership. We highly value:

• Delivering features to production and monitoring their real-world usage.
• Staying abreast of the latest advancements in ML developments.
• Maintaining a healthy balance between rapid iteration and codebase quality.

What You Will Do

• Work in a fully remote environment, with occasional in-person team gatherings a few times a year.
• Focus on specific features, owning the entire process from initial scoping through to production delivery.
• Evaluate innovative ideas and define appropriate metrics to explore, implement, and ship new solutions.
• Contribute significantly to ML models and features, as well as to service architecture and platform scalability.

Qualifications

We are looking for an ML engineer who is product-minded and deeply cares about user outcomes.

• At least 4 years of industry experience in ML/data-science roles, with a specific focus on NLP/generative models and conversational data.
• Demonstrated experience in ML problem-solving, including error diagnosis and hypothesis generation for next steps.
• Hands-on experience shipping ML services using Docker (image building, revision management), GCP services (Cloud Run, instances, Vertex AI), and CI/CD practices.
• Experience with real-time LLM services for RAG conversational systems in production.
• Experience with voice or agentic systems is considered a valuable plus.
• Comfortable working within a compact ML team, embracing shared responsibilities and ownership.

Responsibilities

• Scope, build, and deliver machine learning features directly to production.
• Proactively plan for long-term ML development within the product roadmap.
• Adhere to best practices in software and ML engineering to ensure smooth, efficient operations.

Day-to-day At The Individual Level

• 40% Exploration/Development: Engaging in ML/NLP problem exploration and development.
• 10% Product Alignment: Collaborating with the product team to ensure ML features address the right problems with correct assumptions.
• 30% Production & Maintenance: Shipping new features to production and maintaining live functionalities.
• 20% Free Exploration: Dedicated time for independent investigation and long-term research.

In Your First Month, You Will

• Successfully complete Mavenoid’s comprehensive remote onboarding program.
• Engage with the ML, Product, and Customer Success teams to gain a thorough understanding of ongoing initiatives.
• Familiarize yourself with our platform, product, and established processes.
• Accelerate your understanding of the codebase through co-working sessions and self-study.
• Focus on a specific feature to grasp evaluation metrics and propose improvements in accuracy, efficiency, or performance.

In Your First 3 Months, You Will

• Work on your initial feature improvement, completing the explore, implement, evaluate, and ship loop.
• Collaborate with the team, contributing your insights on system architecture and product enhancements.
• Take ownership of a service, actively pushing its capabilities.
• Tackle one or more new features, from data exploration to feasibility and concept assessment, in partnership with the product lead.

In Your First 6 Months, You Will

• Propose, discuss, coordinate, and implement your first significant platform or architectural change.
• Become proficient with a large portion of the platform, including our CI/CD/evaluation pipeline, machine learning services, and external integrations.
• Assume ownership of a platform segment, proactively identifying areas for improvement.

Our Core Values

• Win as a Team: Collaborate effectively, leveraging individual strengths for collective success and enjoyment.
• Teach & Be Taught: Embrace continuous learning and knowledge sharing to adapt and scale.
• Cut out the B.S.: Prioritize clarity and data to make informed, effective decisions.
• Pick up the Pace: Act with urgency, capitalizing on our startup advantage for speed.
• Eyes on the Ball: Maintain consistent focus on high-impact activities, avoiding distractions.

Key skills/competency

• Machine Learning
• Natural Language Processing (NLP)
• Large Language Models (LLM)
• Generative AI
• Conversational AI
• Python
• GCP
• Docker
• CI/CD
• Data Science