At a Glance
- Tasks: Join our Location AI team to enhance natural language understanding for location-aware applications.
- Company: Mapbox is a leading real-time location platform empowering developers with innovative tools.
- Benefits: Enjoy flexible work options, supportive healthcare, and a culture that values diversity and creativity.
- Why this job: Be at the forefront of AI and spatial intelligence, making a real impact on user experiences.
- Qualifications: 7+ years in engineering, 5+ years in machine learning, strong Python skills, and MLOps experience required.
- Other info: We encourage applicants from all backgrounds to apply and foster an inclusive workplace.
The predicted salary is between 48000 - 84000 £ per year.
Mapbox is the leading real-time location platform for a new generation of location-aware businesses. Mapbox is the only platform that equips organizations with the full set of tools to power the navigation of people, packages, and vehicles everywhere. More than 4 million registered developers have chosen Mapbox because of the platform's flexibility, security and privacy compliance. Organizations use Mapbox applications, data, SDKs and APIs to create customized and immersive experiences that delight their customers.
The Location AI team is working on a product that enables rich, natural, and in-depth explorations about places and all dynamic aspects of the world, continuously refreshed using real-time data from Mapbox. As AI applications become the new primary interface for users, we're building the foundational systems that allow these applications to understand and interact with the physical world in contextually relevant, accurate, and up-to-date ways.
Our team is at the forefront of implementing the model context protocol and AI agent infrastructure that powers this new era of spatial intelligence. We focus on designing interfaces between foundational models and geospatial data, enabling agents to reason about location in ways that feel intuitive and grounded. By treating AI apps as our customer, we craft systems that are modular, scalable, and deeply aligned with how agents learn, plan, and assist.
What You'll Do
- Evaluate and implement models, ensuring optimal performance, latency, and privacy for a location agent's natural language understanding.
- Integrate models into our AI agent framework, enabling agents to interpret and respond to natural language prompts with grounded, context-aware outputs.
- Help design, develop, and manage our MLOps pipeline for training, evaluating, and deploying models for different use cases, languages, and deployment targets.
- Collaborate across engineering, product, and design teams to ensure our machine learning and NLP features meet the evolving needs of AI applications and fit within Mapbox's real-time location intelligence ecosystem.
- Stay at the forefront of NLP and ML research, continuously evaluating and implementing new techniques to enhance our team's capabilities and product user experience.
What We Believe are Important Traits for This Role
- 7+ years of relative industry engineering experience.
- 5+ years of professional experience in machine learning, focused on NLP and transformer models, with an interest in exploring model context protocols.
- Technical Proficiency: Strong programming skills in Python, experience with ML frameworks such as PyTorch or TensorFlow, and experience with MLOps and deploying ML models in production environments hosted on AWS.
- Strong Product Focus: Ability to translate ML capabilities into tangible product features, understanding that your work directly impacts the end-user experience.
- Practical Problem-Solving: Skill in balancing theoretical knowledge with practical implementation, focusing on solutions that work effectively in real-world, in-car environments.
- Collaborative Mindset: Willingness to work closely with product, engineering, and design teams to create integrated solutions that meet user needs and business goals.
- Adaptability and Learning Agility: Enthusiasm for staying current with rapidly evolving ML technologies and ability to quickly apply new learnings to improve our products.
- Iterative Approach: Comfort with rapid prototyping, testing, and refining models based on real-world feedback and usage data.
What We Value
In addition to our core values, which are not unique to this position and are necessary for Mapbox leaders: We value high-performing creative individuals who dig into problems and opportunities. We believe in individuals being their whole selves at work. We commit to this through supportive health care, parental leave, flexibility for the things that come up in life, and innovating on how we think about supporting our people. We emphasize an environment of teaching and learning to equip employees with the tools needed to be successful in their function and the company. We strongly believe in the value of growing a diverse team and encourage people of all backgrounds, genders, ethnicities, abilities, and sexual orientations to apply.
By applying for this position, you acknowledge that you have received the Mapbox Non-US Privacy Notice for applicants. Completing this application requires you to provide personal data, such as your name and contact information, which is mandatory for Mapbox to process your application. We are committed to a fair and equitable hiring process. We do not discriminate against any protected class.
Machine Learning Engineer III, Location AI employer: Mapbox
Contact Detail:
Mapbox Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Engineer III, Location AI
✨Tip Number 1
Familiarise yourself with Mapbox's products and services, especially their Location AI team. Understanding their focus on real-time data and spatial intelligence will help you tailor your discussions during interviews.
✨Tip Number 2
Showcase your experience with NLP and transformer models by discussing specific projects you've worked on. Be prepared to explain how you approached challenges and what impact your solutions had on user experience.
✨Tip Number 3
Highlight your collaborative mindset by sharing examples of how you've worked with cross-functional teams in the past. Emphasising your ability to integrate machine learning capabilities into product features will resonate well with the hiring team.
✨Tip Number 4
Stay updated on the latest trends in machine learning and NLP. Being able to discuss recent advancements or techniques during your interview can demonstrate your passion for the field and your commitment to continuous learning.
We think you need these skills to ace Machine Learning Engineer III, Location AI
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience in machine learning, particularly with NLP and transformer models. Emphasise your programming skills in Python and any experience with ML frameworks like PyTorch or TensorFlow.
Craft a Compelling Cover Letter: In your cover letter, express your enthusiasm for the role and how your background aligns with Mapbox's mission. Mention specific projects where you've implemented models or worked on MLOps pipelines to demonstrate your practical problem-solving skills.
Showcase Collaborative Experience: Highlight instances where you've worked closely with cross-functional teams, such as product, engineering, and design. This will show that you have the collaborative mindset they value and can create integrated solutions.
Stay Current with Trends: Mention any recent advancements in ML or NLP that you've explored. This demonstrates your adaptability and learning agility, which are important traits for the role. You could also discuss how you plan to apply new learnings to improve products.
How to prepare for a job interview at Mapbox
✨Showcase Your Technical Skills
Be prepared to discuss your experience with machine learning, particularly in NLP and transformer models. Highlight specific projects where you've implemented these technologies, and be ready to explain your approach to integrating models into frameworks.
✨Demonstrate Problem-Solving Abilities
Prepare examples of how you've tackled real-world problems using machine learning. Discuss your thought process and the practical solutions you developed, especially in dynamic environments like in-car systems.
✨Emphasise Collaboration
Mapbox values teamwork, so be ready to talk about your experiences working with cross-functional teams. Share how you've collaborated with product, engineering, and design teams to create integrated solutions that meet user needs.
✨Stay Current with ML Trends
Show your enthusiasm for the latest developments in machine learning and NLP. Discuss any recent research or techniques you've explored and how you plan to apply new learnings to enhance products at Mapbox.