Why This Job is Featured on The SaaS Jobs
This Senior ML Engineer role stands out in a SaaS context because it sits at the intersection of product outcomes and platform reliability: translating business goals into measurable proxy metrics, running experiments, and shipping ML services that remain dependable in production. The “Marketplace Efficiency” focus signals work on optimisation problems where model performance directly affects user experience and unit economics, making ML a core part of the product rather than a research sidecar.
From a SaaS career perspective, the emphasis on end-to-end delivery and MLOps maturity builds durable skills: designing systems from scratch, deploying into event-driven environments, and managing concept drift over time. Ownership of operational support and root-cause resolution also develops the practical judgment needed for ML in subscription businesses, where latency, quality, and uptime constraints shape what is worth building. The role’s blend of ML, backend integration, and automation aligns with how modern SaaS teams scale applied AI beyond prototypes.
This position is best suited to engineers who prefer accountable, production-oriented ML work and can communicate trade-offs to non-specialists. It will fit someone comfortable leading ambiguous problem framing, partnering across engineering and product, and mentoring others while keeping services observable and secure.
The section above is editorial commentary from The SaaS Jobs, provided to help SaaS professionals understand the role in a broader industry context.
Job Description
Responsibilities
- Take ownership of the end-to-end machine learning delivery cycle, including building, testing, deploying, and supporting solution components
- Lead the design of complex ML systems from scratch, considering architectural aspects, user needs, and non-functional requirements
- Transform business goals into data science problems and define relevant proxy metrics and non-functional requirements
- Discover and verify business scenarios that can be solved with technical tools and solutions, contributing significantly to the experiment design process
- Manage issues from root cause to resolution, providing feedback to improve engineering design and prevent future issues
- Create and maintain DS-powered services in a production environment, collaborating with other teams and contributing to the backend systems and infrastructure
- Drive automation and track performance and efficiency metrics
- Mentor and onboard junior team members, supporting a culture of continuous learning and best practices
- Communicate complex technical messages clearly and concisely to diverse audiences
- Proactively identify and report potential security, risk, and control issues
- Drive continuous improvement and innovation that leads to business impact
Qualifications
- Comprehensive experience autonomously implementing and leading ML projects, with a proven track record of successes and lessons learned
- Expert-level proficiency in classic machine learning, deep learning, and advanced mathematics
- Strong practical knowledge of MLOps instruments for managing the ML model lifecycle
- Solid software system design skills to contribute to overall architecture, and the ability to design ML systems from scratch
- In-depth experience with event systems and deployment environments, and the ability to maintain services in production
- Proficiency in Python and its frameworks for streaming, batch, and async data processing
- Common knowledge of technologies for backend integration (e.g., Golang)
- A strong grasp of concepts like Concept Drift and its impact on model performance in production
- A strong understanding of data preparation and calculations at all stages of the ML pipeline
Conditions & Benefits
- Stable salary, official employment
- Health insurance
- Hybrid work mode and flexible schedule
- Relocation package offered for candidates from other regions
- Access to professional counseling services including psychological, financial, and legal support
- Discount club membership
- Diverse internal training programs
- Partially or fully paid additional training courses
- All necessary work equipment
