At a Glance
- Tasks: Lead the deployment of cutting-edge machine learning solutions for enterprise clients.
- Company: Join a forward-thinking tech company focused on innovation and client success.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Other info: Dynamic team environment with a focus on collaboration and continuous learning.
- Why this job: Make a real impact by driving the adoption of advanced ML technologies.
- Qualifications: 2+ years in a technical role with strong ML and consulting experience.
The predicted salary is between 60000 - 80000 £ per year.
Requirements
- Education: Bachelor's degree in Computer Science or a related field.
- Experience: 2+ years of experience in a technical, customer-facing role such as Forward Deployed Engineer, or as a Software/ML Engineer with consulting experience.
- ML Engineering & Training Expertise: Experience in the Machine Learning lifecycle (training, optimization, deployment), with a proven ability to lead and execute complex model deployments in production environments.
- Forward Deployed/Consulting Background: Proven track record working within or closely alongside client engineering teams to successfully deploy and integrate complex, high-performance software, involving cloud or on-premise ML workloads.
- Technical & MLOps Knowledge: Understanding of modern ML frameworks, programming languages including Python, and deployment technologies (Docker, Kubernetes, cloud services like SageMaker/Vertex AI/Azure AI).
- Value-Driven Influence: Demonstrated ability to influence senior technical leaders and lead engineers, translating complex model performance and system architectures into clear, tangible business value and deployment assurance.
What the job involves
- You will be the main technical architect responsible for how our most strategic enterprise clients and partners implement and deploy our machine learning solutions.
- As one of our first group of Forward Deployed ML Engineers, you will establish our ML solutions for organizations concerned with the quality, security, performance, and cost of coding models.
- You will leverage your deep ML expertise and technical skills to ensure successful, production-grade implementations, ultimately driving rapid market adoption through proven on-site technical success and client satisfaction.
- End-to-End Ownership: Proactively engage with client or partner teams in Research, Engineering, Data Science, MLOps, Infrastructure to understand their business and technical requirements. With our internal R&D team in the loop, design specific implementations that you will integrate, optimize, and productionize within the client’s existing or greenfield systems as well as transferring technical knowledge to client teams when applicable.
- Subject Expert: Stay up-to-date with the latest LLM capabilities and implementation patterns, you are learning driven. You will need to explain complex technical details and concepts to both technical and non-technical audiences.
- Influence Model Training & Tuning: Represent our core R&D team on-site, leading technical engagement with modern techniques covering all stages of model training using complex, proprietary client data. Ensure architecture is aligned with and optimized for specific constraints (e.g. GPU types, air-gapping).
- Develop Deployment Strategy: Define and execute a global technical strategy for integrating our ML solutions into diverse client environments, ensuring compliance with sector-specific data security standards and performance SLAs. Based on your implementations, build reusable playbooks and libraries that will accelerate yourself and others.
- Building Relationships: Operate autonomously and with agency to build strong relationships with clients, create strategic technical partnerships and drive high-value, referenceable production deployments.
- Serve as Internal Expert: Act as the primary internal consultant, advising product, research, and sales on real-world client infrastructure limitations, performance bottlenecks, and emerging technical standards necessary for product success.
Forward Deployed Engineer (Machine Learning) in London employer: SonarSource
As a Forward Deployed Engineer (Machine Learning) at our company, you will thrive in a dynamic and innovative work culture that prioritises collaboration and continuous learning. We offer competitive benefits, including professional development opportunities and a supportive environment that encourages personal growth, all while working with cutting-edge technology in a location that fosters creativity and engagement. Join us to make a meaningful impact by deploying advanced machine learning solutions for our strategic enterprise clients, ensuring their success and satisfaction.
StudySmarter Expert Advice🤫
We think this is how you could land Forward Deployed Engineer (Machine Learning) in London
✨Tip Number 1
Network like a pro! Get out there and connect with people in the industry. Attend meetups, webinars, or even just grab a coffee with someone who’s already in the role you want. Building relationships can open doors that a CV just can’t.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those related to machine learning. Whether it’s GitHub repos or a personal website, having tangible evidence of your expertise can really impress potential employers.
✨Tip Number 3
Prepare for interviews by practising common technical questions and scenarios. Think about how you’d explain complex ML concepts to non-technical folks. This will not only help you shine in interviews but also show your ability to communicate effectively with clients.
✨Tip Number 4
Don’t forget to apply through our website! We’re always on the lookout for talented individuals like you. Plus, applying directly can sometimes give you an edge over other candidates. So, what are you waiting for? Get your application in!
We think you need these skills to ace Forward Deployed Engineer (Machine Learning) in London
Some tips for your application 🫡
Tailor Your CV:Make sure your CV is tailored to the Forward Deployed Engineer role. Highlight your experience in ML engineering and any consulting roles you've had. We want to see how your skills match up with what we're looking for!
Showcase Your Projects:Include specific projects where you've successfully deployed machine learning models. We love seeing real-world examples of your work, especially if they involved client interactions or complex deployments.
Be Clear and Concise:When writing your application, keep it clear and to the point. Use straightforward language to explain your technical expertise and how it can benefit our clients. We appreciate clarity just as much as complexity!
Apply Through Our Website:Don't forget to apply through our website! It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it’s super easy to do!
How to prepare for a job interview at SonarSource
✨Know Your ML Lifecycle
Make sure you can confidently discuss the entire machine learning lifecycle, from training to deployment. Be prepared to share specific examples of how you've led complex model deployments in production environments, as this will showcase your hands-on experience.
✨Understand Client Needs
Research the company and its clients thoroughly. Understand their business and technical requirements so you can demonstrate how your skills align with their needs. This will help you articulate how you can add value and influence senior technical leaders effectively.
✨Showcase Your Technical Skills
Brush up on your knowledge of modern ML frameworks, programming languages like Python, and deployment technologies such as Docker and Kubernetes. Be ready to discuss how you've used these tools in past projects, especially in client-facing roles.
✨Communicate Clearly
Practice explaining complex technical concepts in simple terms. You’ll need to convey your ideas to both technical and non-technical audiences, so being able to break down intricate details will be key to demonstrating your expertise and building rapport.
