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- Tasks: Design and implement scalable ML architecture for production systems using TensorFlow and Python.
- Company: Join a leading tech firm focused on innovative machine learning solutions.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Why this job: Be at the forefront of ML technology and make a significant impact in commercial software.
- Qualifications: Expertise in TensorFlow, Python, and experience with APIs and performance optimisation.
- Other info: Collaborate with top professionals in a dynamic and supportive environment.
The predicted salary is between 72000 - 108000 £ per year.
We are seeking a highly experienced ML Systems Architect to design and implement a scalable, production-grade architecture for our machine learning solver. This role bridges research prototypes and commercial deployment, ensuring reliability, maintainability, and performance in a mixed technology stack.
Responsibilities
- Architect the ML Solver Platform:
- Define modular architecture for data preprocessing, model execution, and post-processing.
- Establish clear API contracts between Python/TensorFlow and C# services.
- Convert research code into robust, testable, and observable services.
- Implement CI/CD pipelines, automated testing, and reproducibility standards.
- Design REST/gRPC endpoints for cross-language communication.
- Ensure compatibility with C#/.NET services.
- Optimize GPU/CPU utilization, batching strategies, and memory management.
- Plan for multi-model and multi-tenant scenarios.
- Implement model versioning, artifact registries, and deployment workflows.
- Set up monitoring, logging, and alerting for solver performance.
- Apply best practices for secrets management, dependency scanning, and secure artifact storage.
Required Skills & Experience
- ML Frameworks: Expert in TensorFlow (TF2/Keras), experience with ONNX Runtime for inference.
- Programming: Advanced Python for ML; strong understanding of packaging, type checking, and performance profiling.
- APIs: Proficiency in gRPC/Protobuf and REST for cross-language integration.
- Performance Optimization: GPU acceleration (CUDA/cuDNN), mixed precision, XLA, profiling.
- Observability: Metrics, tracing, structured logging, dashboards.
- Security: SBOM, image signing, role-based access, vulnerability scanning.
Preferred Qualifications
- Experience with ONNX Runtime Training, PyTorch, or hybrid ML architectures.
- Familiarity with distributed training strategies and multi-GPU setups.
- Knowledge of feature stores and data validation frameworks.
- Exposure to regulated environments and compliance frameworks.
Tools & Technologies
- ML: TensorFlow, ONNX Runtime, tf2onnx.
- APIs: FastAPI, gRPC.
Why Join Us?
- Work on cutting-edge ML solutions integrated into commercial engineering software.
- Define architecture that scales across global deployments.
- Collaborate with a team of experts in ML, software engineering, and UI development.
Principal Machine Learning Engineer – Production Systems in Bristol employer: SoftInWay Inc
Contact Detail:
SoftInWay Inc Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Principal Machine Learning Engineer – Production Systems in Bristol
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect with potential colleagues on LinkedIn. 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 involving TensorFlow and Python. This gives you a chance to demonstrate your expertise and makes you stand out when chatting with hiring managers.
✨Tip Number 3
Prepare for interviews by brushing up on common ML system architecture questions. Be ready to discuss your experience with CI/CD pipelines and performance optimisation strategies. Practice makes perfect, so consider mock interviews with friends or mentors.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you’re genuinely interested in joining our team and working on cutting-edge ML solutions.
We think you need these skills to ace Principal Machine Learning Engineer – Production Systems in Bristol
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with TensorFlow and Python, as well as any relevant ML frameworks. We want to see how your skills align with the role, so don’t be shy about showcasing your achievements!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you’re passionate about ML systems architecture and how your background makes you a perfect fit for our team. Keep it engaging and personal – we love to see your personality!
Showcase Your Projects: If you've worked on any projects related to ML, especially those involving CI/CD pipelines or performance optimisation, make sure to mention them. We’re keen to see real-world applications of your skills, so include links or descriptions of your work!
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way to ensure your application gets into the right hands. Plus, you’ll find all the details you need about the role and our company culture there!
How to prepare for a job interview at SoftInWay Inc
✨Know Your Tech Stack
Make sure you’re well-versed in TensorFlow, Python, and the other technologies mentioned in the job description. Brush up on your knowledge of APIs, gRPC, and REST, as well as performance optimisation techniques. Being able to discuss these topics confidently will show that you’re a great fit for the role.
✨Showcase Your Problem-Solving Skills
Prepare to discuss specific challenges you've faced in previous projects, especially those related to ML systems architecture. Think about how you’ve converted research code into production-ready services or optimised GPU/CPU utilisation. Real-world examples will help demonstrate your expertise and problem-solving abilities.
✨Understand MLOps and Lifecycle Management
Familiarise yourself with MLOps practices, including model versioning and deployment workflows. Be ready to talk about how you’ve implemented CI/CD pipelines or monitoring solutions in past roles. This knowledge is crucial for ensuring the reliability and maintainability of ML systems.
✨Ask Insightful Questions
Prepare thoughtful questions about the company’s approach to ML architecture and their future projects. This not only shows your interest in the role but also gives you a chance to assess if the company aligns with your career goals. Questions about team collaboration and technology stack can be particularly insightful.
