About Mystic

At Mystic we enable companies to deploy ML models anywhere with just a few lines of code. We abstract the entire infrastructure required to efficiently deploy ML models so that data scientists can focus on their ML models, not servers.

We currently have 2 products. Pipeline Catalyst and Pipeline Core.

Pipeline Catalyst helps developers and startups get their models deployed quickly. They can upload it onto our platform and they get an endpoint that they can use to run inference in their products. Currently powering over 8,000 models for thousands of users.

Pipeline Core helps scale-ups and enterprises deploy their models in their infrastructure of choice. Our production-ready platform brings over 3 years of engineering experience, adds only 40ms of overhead to their ML inference and allows to manage thousands of models and environments at scale.

Engineers are responsible for developing our application in accordance with our roadmap and customer needs, and for designing and implementing robust and scalable development practices. Engineers will set the direction of our product, culture and company.

As a software engineer

You’ll be involved in all aspects of the development and performance of our products. From Pipeline Catalyst to Pipeline Core, building new features, improving performance and maintaining the infrastructure that handles all the API requests. You will help guide the direction of all our products following our users' requirements.

Essential requirements

• BS/MS in Computer Science or a related field;
• 3+ years of professional software engineering experience;
• Experience programming in Python;
• Experience with designing high-performing, reliable, and scalable backend systems. We are looking for engineers that can own a feature from start to finish; and
• Experience working with cloud technologies (Docker, Kubernetes, AWS, GCP, Azure, etc.).

Not essential but nice to have

• Experience contributing to open source projects and/or related communities;
• Experience with software security and data sensitive applications.
• Experience with PyTorch, Tensorflow and other deep learning and classical ML frameworks;
• Experience with products in the cloud infrastructure and MLOps landscape; and
• Experience with building large scale data pipelines (Kafka, Spark, Hadoop, Airflow, etc.).

Benefits

• Competitive salary;
• Equity compensation;
• Huge impact, as an early-stage startup your daily work will have a direct impact in the company's success;
• Whatever you want to learn about, we'll make it happen;
• Cycle to work scheme;
• Flexible work-hours; and
• The job is hybrid (in-office at least 3-days a week) and onsite at our offices in London.