Responsibilities
- Define the technical vision and standards for machine learning initiatives
- Make key architectural choices for ML platforms and services
- Evaluate and authorize system design proposals
- Detect and resolve accumulated technical shortcomings
- Promote industry best practices in machine learning engineering
- Diagnose and resolve advanced technical obstacles
- Assess and integrate emerging tools and technologies
- Coach early- and mid-career machine learning engineers
- Perform in-depth code evaluations for quality and performance
- Advise on effective strategies for solving technical problems
- Support team members in resolving intricate system issues
- Design pathways for professional development and skill growth
- Disseminate expertise via training sessions and written materials
- Strengthen team-wide technical capabilities
- Deliver high-impact code for critical system components
- Develop proof-of-concept implementations for novel methods
- Own the most technically risky project elements
- Build reusable frameworks and tools to accelerate ML development
- Sustain credibility through hands-on coding contributions
Requirements
- Deep expertise across several machine learning disciplines
- Proven track record developing ML systems for production environments
- Capable of designing ML architectures that are scalable and easy to maintain
- Solid grasp of ML infrastructure and operational workflows
- Hands-on experience with large language model applications and retrieval-augmented generation
- Commitment to high coding standards and engineering rigor
- Skilled in TensorFlow, PyTorch, and scikit-learn
- Extensive experience with AWS, with exposure to alternative cloud platforms
- Familiarity with data pipeline design and infrastructure
- Able to architect complex distributed systems
- Experience improving efficiency of models and supporting infrastructure
- Produces clean, well-structured, and sustainable code
- Advocates for robust testing, including unit, integration, and ML-specific validation
- Mastery of advanced Git workflows and team collaboration practices
- Built and managed end-to-end ML pipelines in production
- Produces thorough and accessible technical documentation
Benefits
- Sustained B2B engagement
- Fully remote working model
- Financial support for medical insurance
- Paid time off for illness, vacation, and public holidays
- Ongoing learning support, including unlimited AWS certification funding
Work Arrangement
Remote (Worldwide)
Team
Team size: 2-5 engineers
