About Flowcode
Flowcode is the offline-to-online technology platform reinventing how brands connect to consumers in the physical world. With our powerful enterprise grade QR and data analytics infrastructure, we enable enterprise clients to activate, engage, convert, and measure offline audiences in real time. We're growing rapidly — and as we move upmarket, elite-level storytelling is at the heart of our sales strategy.
About the Role
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.
Responsibilities
As part of the Flowcode Machine Learning team, you will:
- 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.
Qualifications
Required
- 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).
Preferred
- Familiarity with system architecture and common service paradigms (e.g., CRUD, cron-jobs).
- Familiarity with experiment design, including A/B testing and causal inference methods.
- Experience with MLOps tools (e.g., MLflow, Kubeflow, SageMaker).
- Comfort with visualization and presentation of high-dimensional data using modern tools.
- Background in statistics, optimization, or computer vision is a plus.
- Experience with clustering and anomaly detection using various supervised and unsupervised techniques.
- Experience with datastores such as Snowflake, PostgreSQL, Singlestore, Redis, Milvus, etc.
- Experience with streaming technologies such as Kafka, Flink, NATs, etc.
Location:
Remote or Hybrid in NYC Office
The current range for this role is up to $215-250K OTE plus equity. #LI-DNI
