Turn ambiguous client problems into shipping code.
Drive projects from discovery to deployment
Collaborate with client and internal project teams
Design and write clean, scalable code at appropriate quality standards (sometimes “right”, and sometimes “right now”).
Design, integrate, and productionize ML solutions including predictive models, GenAI systems, physics-informed ML, and digital twins.
Collaborate with domain experts in energy, real estate, and climate to translate business needs into ML solutions
Advocate for engineering best practices and positive dev culture
Requirements
5+ years building and deploying ML systems in production environments
Expert-level Python and experience with PyTorch / TensorFlow
Deep expertise in at least one domain: NLP, Computer Vision, Time-Series, or Reinforcement Learning
Generative AI and LLM-related capabilities (e.g., prompt engineering, RAG, fine-tuning, LangChain, model evaluation tooling)
MLOps and infrastructure automation (e.g., CI/CD for ML, Docker, Kubernetes, Terraform, MLflow, Kubeflow)
Strong engineering fundamentals: system design, scalability, testing, and monitoring
Track record of translating ambiguous business problems into production ML solutions
Experience with cloud ML platforms (AWS SageMaker, GCP Vertex AI, or Azure ML)
Champion for quality (model validation, reproducibility, monitoring, bias/variance checks)
Benefits
Competitive early-stage startup compensation (based on capabilities, experience, and location)
Bonus eligibility
Health insurance with meaningful coverage for dependents
Flexible paid time off
Equity
Fully remote culture with a cluster of teammates in Seattle
Training and learning opportunities
Be part of a fast-growing, profitable, mission-driven company with industry-leading clients tackling the massive opportunity of AI transformation in critical industries.
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.