About Mystic

At Mystic we’ve built the API for serverless ML compute (deployed as Pipeline AI: https://www.pipeline.ai/) . We abstract the entire infrastructure required to efficiently deploy ML models so that data scientists can focus on adding value, not servers. We’re building a strong engineering team and are at an important inflection point. 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 API. From our python client, integrating into the wider MLOps landscape, building tools to optimise internal performance through to improving and maintaining the infrastructure that handles the API requests. You will help guide the direction of the API to meet the MLOps community 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;
• Experience working with cloud technologies (Docker, Kubernetes, AWS, GCP, Azure, etc.).

Not essential but nice to have

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

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 onsite at our office in London.