Staff Machine Learning Engineer, Flowcode

• 5+ years of software or ML engineering experience, including 3+ years working on production ML systems
• Strong skills in Python and SQL, with fluency in ML tools such as PyTorch, scikit-learn, transformers, and pandas
• Hands-on experience with LLMs and transformer architectures (e.g., GPT, BERT), including embedding generation and prompt engineering
• Solid experience deploying ML models into production using Docker, Kubernetes, or cloud-based pipelines (e.g., AWS, GCP)
• Experience building end-to-end solutions using LLMs for various applications such as RAG, classification, and summarization pipelines
• Strong data wrangling and engineering foundations; comfortable working with unstructured and complex data
• Excellent communication skills and a track record of working cross-functionally with product and engineering teams
• Experience with traceability and observability tools in deployed systems (e.g., Langfuse, Datadog)

Desirable

• We’re seeking a passionate Staff Machine Learning Engineer to join Flowcode’s growing ML team. You’ll design and deploy models, contribute to core product and analytics systems, and help shape strategic decisions.
• The role involves working with structured and unstructured data, building scalable ML infrastructure, and setting best practices in code quality and experimentation.
• Ideal candidates thrive in fast-paced, greenfield environments and collaborate closely with engineering, product, and analytics to drive impact at scale
• Design and deploy ML models using traditional supervised, unsupervised, and semi-supervised techniques on unstructured, multidimensional datasets
• Design and deploy solutions that utilize open-source and proprietary LLMs (Anthropic, OpenAI, Ollama, etc.)
• Create insightful visualizations using tools like matplotlib, seaborn, plotly, etc
• Build and scale ML infrastructure for analytics, consumer products, business intelligence, and model pipelines
• Develop with Python, SQL, and libraries such as PyTorch, Langchain, huggingface-transformers, scikit-learn, and pandas
• Demonstrate technical ownership of end-to-end ML-based solutions
• Collaborate with engineering, product, and analytics teams to ensure successful deployment and adoption of ML-driven solutions
• Drive project scoping and build novel ML applications and pipelines