At a Glance
- Tasks: Deploy and optimise AI systems in real-world industrial settings.
- Company: Join a pioneering tech company focused on sustainable energy solutions.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Other info: Collaborative team environment with exciting projects and career advancement potential.
- Why this job: Make a tangible impact in the energy sector with cutting-edge AI technology.
- Qualifications: MSc in relevant field or equivalent experience; strong Python skills required.
The predicted salary is between 60000 - 80000 £ per year.
Applied Computing was founded in 2024 to build Orbital, a physics-informed foundation model for energy operations. We’re live across oil and gas, refineries, and petrochemicals, working towards our mission: sustainable abundance for a growing planet. The hydrocarbon industry keeps the world running. But its complexity has left operators tied to legacy systems, making critical decisions on less than 10% of available data. We built Orbital to change that. It’s a foundation model built specifically for energy that lets companies use AI at scale, harnessing all of their operational data and optimising in real time for any metric. Decisions get faster, operations get safer, and carbon intensity falls.
As a Forward Deployed ML Engineer, your job is to make Orbital’s AI systems work in customer reality. You will deploy, configure, tune, and operationalise our deep learning models inside live industrial environments; spanning cloud, on-premise, hybrid, and air-gapped infrastructure. This is not a pure research role. You are not training experimental models in isolation. You are adapting production AI systems to customer data, configuring agents and RAG pipelines, tuning anomaly detection, and ensuring models deliver value in production workflows. If Research builds the models, you make them work on-site.
Operating Context
Forward Deployed ML Engineers operate in pods of three alongside:
- Full Stack Engineers
- Data Engineers
Each pod delivers 2–3 customer deployments per quarter, owning AI configuration, model tuning, agent orchestration, and inference reliability in production.
Requirements
- MSc in Computer Science, Machine Learning, Data Science, or related field, or equivalent practical experience.
- Strong proficiency in Python and deep learning frameworks (PyTorch preferred).
- Solid software engineering background; designing and debugging distributed systems.
- Experience building and running Dockerised microservices, ideally with Kubernetes/EKS.
- LLM API integrations (OpenAI, Claude, Gemini), FastAPI for ML services and REST inference APIs.
- Familiarity with message brokers (Kafka, RabbitMQ, or similar).
- Comfort working in hybrid cloud/on-prem deployments (AWS, Databricks, or industrial environments).
- Exposure to time-series or industrial data (historians, IoT, SCADA/DCS logs) is a plus.
- Domain experience working as a data scientist in oil and gas or energy is a plus.
- Ability to work in forward-deployed settings, collaborating directly with customers.
- Comfortable in customer-facing technical roles.
- Able to operate in forward-deployed environments.
- Strong troubleshooting capability in production AI systems.
What Success Looks Like
- AI systems are deployed and running in customer environments.
- Models are tuned to customer data and delivering operational value.
- Anomalies and predictions are trusted by engineers.
- Multi-agent copilots function reliably in production workflows.
- RAG systems retrieve accurate, domain-relevant insights.
- Inference pipelines run with high uptime and low latency.
ML Engineer (Forward Deployed Engineering) employer: Applied Computing Technologies
At Applied Computing, we pride ourselves on being an exceptional employer, offering a dynamic work culture that fosters innovation and collaboration. As a Forward Deployed ML Engineer, you will have the unique opportunity to work directly with clients in the energy sector, deploying cutting-edge AI solutions that drive real-world impact. With a strong focus on employee growth and development, we provide ample opportunities for professional advancement while contributing to our mission of sustainable abundance for a growing planet.
Contact Details:
Applied Computing Technologies Recruitment Team
StudySmarter Expert Advice🤫
We think this is how you could land ML Engineer (Forward Deployed Engineering)
✨Tip Number 1
Network like a pro! Get out there and connect with folks in the industry. Attend meetups, webinars, or even local tech events. You never know who might have the inside scoop on job openings or can put in a good word for you.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those related to ML and AI. Share your GitHub link when you chat with potential employers; it’s a great way to demonstrate what you can do beyond just a CV.
✨Tip Number 3
Prepare for technical interviews by practising coding challenges and system design questions. Use platforms like LeetCode or HackerRank to sharpen your skills. Remember, they want to see how you think and solve problems, so talk through your thought process!
✨Tip Number 4
Don’t forget to apply through our website! We’re always on the lookout for talented individuals like you. Tailor your application to highlight your experience with deploying AI systems and working in customer-facing roles, as that’s what we value at Applied Computing.
We think you need these skills to ace ML Engineer (Forward Deployed Engineering)
Some tips for your application 🫡
Show Your Passion for AI:When you're writing your application, let your enthusiasm for AI and machine learning shine through. We want to see how excited you are about deploying models in real-world settings and making a difference in the energy sector!
Tailor Your Experience:Make sure to highlight your relevant experience in Python, deep learning frameworks, and any hands-on work with Docker or Kubernetes. We love seeing how your background aligns with our needs, so don’t hold back on those details!
Be Clear and Concise:Keep your application straightforward and to the point. We appreciate clarity, so avoid jargon unless it’s necessary. Remember, we’re looking for someone who can communicate effectively, especially in customer-facing roles.
Apply Through Our Website:Don’t forget to submit your application through our website! It’s the best way for us to keep track of your application and ensure it gets the attention it deserves. We can’t wait to hear from you!
How to prepare for a job interview at Applied Computing Technologies
✨Know Your Tech Inside Out
Make sure you’re well-versed in Python and the deep learning frameworks mentioned in the job description, especially PyTorch. Brush up on your knowledge of Docker, Kubernetes, and any relevant APIs. Being able to discuss your hands-on experience with these technologies will show that you’re ready to hit the ground running.
✨Understand the Industry Context
Familiarise yourself with the oil and gas sector and how AI is transforming it. Knowing about the challenges faced by operators and how Orbital can help will demonstrate your genuine interest in the role and the company’s mission. It’s all about showing that you understand the bigger picture!
✨Prepare for Customer-Facing Scenarios
Since this role involves working directly with customers, think about past experiences where you’ve had to explain complex technical concepts to non-technical stakeholders. Be ready to share examples that highlight your communication skills and ability to adapt your approach based on the audience.
✨Showcase Your Problem-Solving Skills
Be prepared to discuss specific challenges you’ve faced in deploying or tuning AI systems in production. Use the STAR method (Situation, Task, Action, Result) to structure your answers. This will help you convey your troubleshooting capabilities and how you ensure reliability in production workflows.
