Senior Machine Learning Engineer
United Kingdom / Ireland
Product – Engineering /
Mavenoid is the Intelligent Support Platform for products and devices. Our purpose-built technology delivers best-in-class customer support with intelligent troubleshooting and personalized remote support. Our product manufacturers and sellers provide genius-level customer support and realize brilliant cost savings.
Having raised our Series B, we’re in an exciting phase of growth but are still small enough for each new person to have a big impact on the company as a whole. We were founded in Stockholm, Sweden, but now operate globally as a remote-first company, with team members located across Sweden, the United Kingdom, The United States, and more.
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 (more than 600Ks conversations or 2Ms of search queries per month), and documents. You will help to process this data and assess new NLP models to build and improve the set of ML features in the products.
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-
- Work fully remote and meet in real life from time to time
- 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 to Machine Learning models and features but also service architecture and the platform at scale
- In Python
- With NLP/ML libs, including Spacy, Huggingface, Pytorch and OpenAI (among others)
- Deploying with docker on GCP cloud services
We are pragmatic about which tool to use for each approach, as long as it can be properly packaged for production.
- Scope, build, and deliver ML features to production
- Following software and ML engineering best practices to keep things humming
- Thinking ahead for long-term ML development in the product.
- 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 features alive and well
- 20% free exploration/investigation for long term
Day-to-day at the individual level:
- You are an ML engineer who cares about product and user outcomes
- At least 4 years of industry experience in Machine Learning roles, specifically in NLP and with conversational data
- Experience with ML problem-solving, diagnosing errors and hypothetising next steps
- Experience with real-time ML services for search or recommendation systems (bonus for generative LLM).
- Experience with shipping ML services using Docker (build images, manage revisions), GCP services (cloud run, instances, vertex) and CI/CD practices
- Experience with working in a compact remote ML team with shared responsibilities & ownership
What we offer
- Employees’ work-life balance: flexible working hours and 25 days paid leave + national bank holidays.
- Employees’ wellness: meal allowance, bi-weekly meditation sessions, and up to $500 as a Health Perk.
- Remote/home setup: latest generation Mac, large screens, ergonomic desks/chairs, and other equipment you might need.
- A career opportunity where your professional growth is in focus: unlimited books, three days off per year to focus on personal development, and up to $2,400 for courses, conferences, and other learning material.
- Regular company trips and remote activities.
Our Core Values
Win as a Team - Teams are not just extensions of leaders, but are instead a composite of individuals, each with their own strengths. We need to work together in order to scale. And, it’s more fun this way!
Teach & Be Taught - As a company, we believe that there is always a way to improve how you work, and therefore it is important to be consistently open to learning. Also, in a fast-growing company, roles & contexts shift, and it is important to be able to adjust. Teaching others is the flip side of that – it's a great way to build deeper understanding of the topics you teach, and also an important tool to scale the organization as you step into new roles and responsibilities.
Cut out the B.S. - Get to the bottom of the issue and surface the information needed to make good decisions.
Pick up the Pace - As a startup, our structural advantage is speed, but only if we act with urgency. Don’t leave for tomorrow what you can do today.
Eyes on the Ball - Don’t get distracted from what is most important, right now. In a startup, there are a million different things that you can do in any situation, and it’s necessary to have a consistent focus over time on the things that have the highest impact.