Senior Software Engineer - Machine Learning

You’ll be a generalist responsible for building and running large-scale data, machine learning, and agentic systems. The focus is operational ML/AI, including agentic systems and geospatial data pipelines. You should be comfortable owning the full lifecycle: from data ingestion and distributed processing to model development, deployment, and monitoring. This role requires the ability to iterate quickly from initial concept to a robust, production-ready solution. **Key Responsibilities** * Take ownership of the end-to-end AI/ML lifecycle, with a strong focus on dealing with complex and messy data, thorough evaluation of different approaches, and successfully deploying robust models, and handling cost vs performance tradeoffs. * Implement and integrate large-scale, agent-based systems with access to external systems, building these solutions from the ground up and integrating them with our existing infrastructure. * Establish observability for pipelines, models, and agents (metrics, tracing, alerting). * Collaborate with product and customer teams to drive revenue. * Strong experience with distributed data processing, particularly Spark and SQL. * Proven expertise in building production machine learning systems, including working with large, wide datasets, effective training, deployment, and monitoring. * Experience designing and deploying task-oriented AI agents and working with coding agents. * Experience working with cloud services across data, compute, and ML. * Strong communication abilities, including code architecture and documentation, at a level where any technical team member can troubleshoot and contribute easily. **Languages:** Scala, Python **Tools / Frameworks:** Spark, AWS Sagemaker / Bedrock, Kubernetes **Nice to Haves** * Startup experience or growing projects from 0 to production in a larger org. * Experience with large geospatial datasets, formats, and indexing strategies. * Experience building operational AI agents that work at scale (millions of separate, complex tasks including web research) * Experience with fine-tuning, distilling, and self-hosting LLM models. * Experience in traditional ML, with a focus on working with messy data and robust evaluation of model approaches. * Proficiency with CI/CD, infrastructure as code, and containerization. **What Success Looks Like** * ML/AI models deployed with robust monitoring and significant customer impact. * Agentic workflows improving internal/external operations. * Infrastructure that is stable, observable, and automated. * Successful iteration and delivery of new ML/AI products from concept to production. * Ability to contribute to existing geospatial pipelines directly or through the use of AI