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For Companies At a Glance
- Tasks: Shape innovative ML features to enhance global hardware support.
- Company: Join Mavenoid, a forward-thinking tech company with a collaborative spirit.
- Benefits: Enjoy remote work, competitive salary, and opportunities for professional growth.
- Why this job: Make a real impact in the ML field while working with cutting-edge technologies.
- Qualifications: 4+ years in ML/NLP, experience with Docker and GCP services.
- Other info: Dynamic team environment with exciting career advancement potential.
The predicted salary is between 36000 - 60000 £ per year.
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.
Technical Stack:
- Python
- NLP/ML libraries (langchain, langfuse, huggingface, PyTorch, etc.)
- LLM providers (OpenAI, Anthropic, Google, Mistral) and hosted models
- Docker on GCP cloud services
Way of Working:
- Small team with shared responsibilities
- Focus on shipping to production and seeing usage
- Keep up with ML developments and balance speed and code quality
You Will:
- 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
Qualifications:
- 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
Responsibilities:
- 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
Day-to-Day At The Individual Level:
- 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
Onboarding Timeline:
- First month: complete remote onboarding, meet teams, familiarise 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.
Seniority Level: Mid-Senior level
Employment Type: Full-time
Job Function: Engineering and Information Technology
Industries: Software Development
Senior Machine Learning Engineer employer: Mavenoid
Contact Detail:
Mavenoid Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior Machine Learning Engineer
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect with other ML engineers. You never know who might have the inside scoop on job openings or can refer you directly.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your ML projects, especially those involving NLP and LLMs. This will give potential employers a taste of what you can do and how you think.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and problem-solving skills. Practice coding challenges and be ready to discuss your past projects and how they relate to the role at Mavenoid.
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets seen. Plus, it shows you’re genuinely interested in joining our team and contributing to our mission.
We think you need these skills to ace Senior Machine Learning Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with ML and NLP, especially any projects that involved conversational data. We want to see how your skills align with what we’re looking for, so don’t be shy about showcasing relevant achievements!
Craft a Compelling Cover Letter: Your cover letter is your chance to tell us why you’re the perfect fit for the Senior Machine Learning Engineer role. Share your passion for ML and how you’ve contributed to product outcomes in the past. Keep it engaging and personal!
Showcase Your Technical Skills: Since we’re all about Python and various ML libraries, make sure to mention your proficiency with tools like Docker, GCP, and any LLM providers you’ve worked with. We love seeing practical examples of how you’ve used these technologies in your previous roles.
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it shows us you’re keen on joining our team!
How to prepare for a job interview at Mavenoid
✨Know Your Tech Stack
Make sure you’re well-versed in the technical stack mentioned in the job description. Brush up on Python, NLP/ML libraries like langchain and PyTorch, and be ready to discuss your experience with LLM providers. This will show that you’re not just a fit for the role but also genuinely interested in the tools they use.
✨Showcase Your Problem-Solving Skills
Prepare to discuss specific ML problems you've tackled in the past, especially those involving conversational data. Be ready to explain your thought process when diagnosing errors and how you hypothesised next steps. This will demonstrate your hands-on experience and analytical thinking.
✨Emphasise Team Collaboration
Since the role involves working in a small team with shared responsibilities, highlight your experience in collaborative environments. Share examples of how you’ve contributed to team projects, especially in shipping ML features to production. This will show that you can work well with others and are committed to collective success.
✨Prepare for Real-Time Scenarios
Given the focus on real-time LLM services, think of scenarios where you’ve implemented or improved such systems. Be ready to discuss the challenges you faced and how you overcame them. This will illustrate your practical knowledge and readiness to tackle similar challenges at Mavenoid.
