[Hiring] Machine Learning Lead @Mayflower

Role Description

We are looking for a Lead Machine Learning Engineer focused on building and delivering production-grade ML-powered applications. This role is not about research or experimentation. You will be responsible for turning ML models into reliable, scalable, user-facing services. You will work at the intersection of machine learning and backend engineering, owning the full lifecycle of ML-powered features — from integration to deployment and operation in production.

• Design and build production ML services (APIs, microservices, real-time systems)
• Integrate ML models into user-facing applications
• Ensure reliability, scalability, and performance of ML-powered systems
• Define and implement best practices for serving, versioning, and monitoring models in production
• Collaborate closely with:
  • ML engineers / data scientists (who develop models)
  • DevOps / MLOps team (who provide platform and infrastructure)
• Own the delivery pipeline of ML features into production
• Lead and mentor engineers working on ML-powered applications
• Drive architectural decisions around low-latency and high-load systems
• Identify bottlenecks between experimentation and production and eliminate them

Qualifications

• 5+ years of experience in software engineering / ML engineering
• Strong experience building production backend systems (Python preferred)
• Hands-on experience deploying and serving ML models in production
• Experience with API development (FastAPI, Flask, gRPC, etc.)
• Understanding of system design, scalability, and high-load environments
• Experience working with ML pipelines and model lifecycle
• Ability to bridge the gap between ML and engineering teams
• Experience in a Lead or Senior role (mentoring, ownership, decision-making)

Requirements

• Experience with real-time ML systems (recommendations, ranking, personalization)
• Experience in video streaming or high-throughput platforms
• Familiarity with Kubernetes and cloud/on-prem environments
• Experience with feature stores, model monitoring, A/B testing
• Experience working alongside MLOps platforms (MLflow, Airflow, etc.)

Benefits

• Full remote work
• Competitive compensation with a service contract
• Transparent performance reviews twice a year, with bonus opportunities and salary adjustments
• An annual allowance for home office improvements, sports activities, equipment upgrades, and more
• Investment in your development: paid language courses, access to various learning platforms, and a mindfulness benefit, including psychological support with 50% coverage
• Career growth in a fast-scaling project with opportunities to influence technical decisions
• A culture of recognition: our peer reward program celebrates contributions from across the team