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Senior Machine Learning Engineer
Takeaway.com. Collaborate with Data Scientists, Product Managers, and Backend Engineers to connect model development with production systems .
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in designing and operating machine learning systems in production, with a strong focus on MLOps practices, cloud-native platforms, and software engineering principles. Capable of leading projects, mentoring peers, and ensuring system reliability and stability throughout the development lifecycle.
Highest-signal resume keywords
Proficiency In PythonMLOps PracticesCloud-Native ML PlatformsInfrastructure As CodeContainerization Using Docker And Kubernetes
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Machine Learning Systems DesignObject-Oriented DesignDesign PatternsTesting Applied To Machine LearningREST APIsBatch InferenceEvent-Driven SystemsSQLDistributed Data Processing
Soft Skills
Communication With StakeholdersMentoring PeersProactive Automation
Tools & Technologies
AirflowDagsterAWS SageMakerGCP Vertex AITerraformDockerKubernetes
Industry Keywords
MLOpsData Engineering FundamentalsCI/CD For MLModel VersioningFeature Stores
Tech Stack
Tools & technologiesAirflowAWSCloudDockerGoogle Cloud PlatformKubernetesPythonSQLTerraform
About the role
Key responsibilities & impact- Collaborate with Data Scientists, Product Managers, and Backend Engineers to connect model development with production systems
- Lead architectural design of end-to-end ML systems, including data ingestion, training pipelines, real-time inference, and monitoring infrastructure
- Transform data science prototypes into robust, scalable, and secure production software
- Own the path to production for machine learning systems
- Drive adoption of MLOps practices, including CI/CD for ML, model versioning, and feature stores
- Communicate ML system complexities and latency-versus-accuracy trade-offs to technical and non-technical stakeholders
- Build and maintain relationships across Data and Engineering organizations
- Ensure ML systems integrate with the wider platform
- Lead projects, mentor peers, and promote engineering excellence
- Take responsibility for work from discovery through production, including system reliability and stability
Requirements
What you’ll need- Proficiency in Python
- Strong understanding of software engineering principles, including object-oriented design, design patterns, and testing, applied to Machine Learning
- Demonstrable experience designing and operating ML systems in production
- Familiarity with serving patterns including REST APIs, batch inference, and event-driven systems
- Experience with orchestration tools such as Airflow or Dagster
- Experience with cloud-native ML platforms such as AWS SageMaker or GCP Vertex AI
- Ability to influence infrastructure and tooling decisions, including build-versus-buy discussions
- Strong knowledge of Infrastructure as Code, particularly Terraform
- Experience with containerization using Docker and Kubernetes
- Familiarity with data engineering fundamentals, including SQL and distributed data processing
- Proactive approach to automating manual processes
- Passion for improving the developer experience for Data Scientists
Benefits
Comp & perks- Hybrid work arrangement: 3 days a week from office and 2 days working from home
- Inclusive culture focused on diversity, belonging, and employee wellbeing
- Mentoring and professional growth opportunities
- Supportive, dynamic, fast-paced work environment
- Opportunity to work on systems impacting millions of customers daily