About the Role
This role involves managing a team of machine learning engineers, guiding technical direction, and ensuring the delivery of high-impact machine learning solutions. The position bridges engineering leadership with hands-on technical oversight in a fast-paced environment.
Responsibilities
- Lead and mentor a team of machine learning engineers through project execution and career development
- Collaborate with product and research teams to define machine learning roadmaps
- Oversee the design and implementation of scalable model training and serving infrastructure
- Ensure machine learning systems meet performance, reliability, and monitoring standards
- Drive best practices in model versioning, reproducibility, and deployment pipelines
- Evaluate new tools and frameworks for integration into the ML stack
- Facilitate cross-team technical alignment on data strategy and model integration
- Manage sprint planning, technical milestones, and delivery timelines
- Promote a culture of experimentation, code quality, and continuous improvement
- Troubleshoot production issues related to machine learning models and data pipelines
- Support the integration of ML outputs into customer-facing applications
- Champion ethical AI practices and model transparency
- Recruit, interview, and onboard new engineering talent
- Conduct performance reviews and create individual development plans
- Represent the team in executive and technical forums
- Balance technical debt with feature development priorities
- Stay current with advancements in machine learning and distributed systems
- Foster psychological safety and inclusive team dynamics
- Coordinate with data scientists and data engineers to ensure pipeline reliability
- Define success metrics for ML-driven features and monitor impact
Nice to Have
- Master’s or PhD in computer science, statistics, or related field
- Prior experience building real-time inference systems
- Leadership experience in startups or high-growth environments
- Published work in machine learning or software engineering venues
- Direct experience with geospatial or remote sensing data
- Background in environmental or climate technology domains
Compensation
$180,000 - $220,000 annually, depending on experience
Work Arrangement
Hybrid remote with team hubs in select locations
Team
Part of the core machine learning engineering group focused on developing production-grade AI models
Our Tech Stack
- We use Python as the primary language for model development and services
- Our infrastructure runs on Google Cloud Platform with Kubernetes orchestration
- We rely on BigQuery and Dataflow for large-scale data processing
- Model training pipelines are managed through Vertex AI and custom tooling
- We use MLflow for experiment tracking and model registry
- Frontend integrations are built with React and RESTful APIs
Team Culture
- We value transparency, ownership, and continuous learning
- Engineers are encouraged to lead initiatives and propose technical improvements
- Weekly tech talks and knowledge-sharing sessions are standard practice
- We maintain a blameless postmortem culture for incident reviews
- Team decisions are made collaboratively with input from all levels
Available for qualified candidates
