Applied Scientist, Orbital Sidekick

• We're seeking an Applied Machine Learning Engineer who thrives at the intersection of theory and practice
• This role is perfect for builders who are equally comfortable diving into ML theory, developing internal tools from scratch, and architecting production pipelines
• 3+ years of experience as an applied ML engineer or research engineer
• BS, MS, or PhD in Computer Science, Engineering, Physics, Mathematics, or related field
• Theory meets practice: Strong foundation in ML theory combined with hands-on experience building production systems
• Full-stack mindset: Comfortable developing internal tools, APIs, and user interfaces that solve real problems
• Pipeline expertise: Proven experience architecting and maintaining complex workflows in orchestration frameworks
• Ownership mentality: Track record of identifying needs, proposing solutions, and driving projects to completion
• Production experience: Hands-on experience deploying, monitoring, and maintaining ML models in cloud environments (AWS)

Desirable

• You'll operate as both an applied scientist and systems builder, taking end-to-end ownership of ML solutions. We're looking for someone who can spot inefficiencies, architect solutions, and build the tools needed to scale our capabilities
• You'll have the autonomy to identify gaps, propose solutions, and drive them to completion in a fast-moving startup environment
• Identify and solve real problems: Collaborate with scientists to understand needs, then design and build the infrastructure to address them
• Build production ML systems: Develop robust pipelines using orchestrators (Prefect, Airflow, Dagster) that move models from research to production
• Create internal tools: Build developer-friendly tools that accelerate team productivity and model development workflows
• Own the ML infrastructure: Design and maintain model repositories, experiment tracking, monitoring, and deployment systems
• Bridge theory and practice: Apply ML theory to solve practical problems while building scalable, production-ready solutions
• Optimize for impact: Focus on cloud resource optimization, model performance, and system reliability that directly supports product goals
• Drive continuous improvement: Establish CI/CD, automated testing, and monitoring systems that ensure robust, reliable deployments
• Technical Stack:
• Python, PyTorch, Weights & Biases, MLFlow, DVC, Ray, Dask
• Experience with workflow orchestrators (Prefect, Airflow, Dagster)
• Cloud platforms (AWS) and containerization (Docker, Kubernetes)
• Software engineering best practices (testing, CI/CD, version control)