Founding Machine Learning & Data Engineer (Forward-Deployed)

About The Company

Melow is building the AI Operating System for data - a dynamic ontology engine that makes sense of all forms of structured and unstructured data and powers a new generation of AI Workers capable of reasoning, planning, and acting inside real enterprise systems. We’re the AI middleware for the world, helping data-rich companies transform their data into intelligent, autonomous systems. We’re still in stealth, but already working with some of the world’s most recognized companies across fintech, travel, insurance, gaming, and logistics. Our mission: define a new software category that brings intelligence directly into enterprise data. We have traction, we’re backed by top investors, and now we’re building the next frontier of machine learning and data automation.

About The Role

Design, train, and deploy ML models for real-world use cases, build data infrastructure, and engage with customers to implement AI solutions. This is a hands-on role at the intersection of machine learning, data engineering, and customer delivery. You’ll design, train, and deploy ML models for real-world customer use cases, while building the data infrastructure that powers them. You’ll operate like a forward-deployed engineer — directly engaging with customers, understanding their business problems, and translating them into production-grade ML systems. You’ll work closely with Melow’s CEO, CTO, and founding engineers to define how intelligence is embedded across our AI infrastructure and customer implementations. This is a role for someone who thrives in early-stage chaos, ships fast, and wants to shape how enterprises use AI in practice.

What You’ll Own

• End-To-End ML Systems: Build, train, and deploy predictive models across structured and unstructured data. Own the full lifecycle from data exploration to model optimization and evaluation.
• Core Model Development: Implement, optimize, and deploy models for regression, classification, clustering, and deep learning frameworks.
• Data Infrastructure & Pipelines: Architect and maintain scalable data pipelines, schema models, and ETL processes to support enterprise-scale ML workflows.
• Customer Deployment: Work hand-in-hand with enterprise clients to design custom ML strategies, run experiments, and integrate models into production systems.
• Intelligent Data Systems: Build the bridge between semantic layers, knowledge graphs, and feature stores to create real-time, intelligent feedback loops for ML.
• ML Ops & Observability: Establish frameworks for model versioning, monitoring, retraining, and interpretability.
• Thought Partnership: Collaborate with leadership to define the ML & Data strategy that powers Melow’s AI Workers and enterprise deployments.

Requirements

• 7–10+ years of experience in machine learning and data engineering roles
• Experience in early-stage startups and established AI-driven companies
• Strong foundations in supervised/unsupervised learning, optimization, and evaluation
• Experience with feature engineering, clustering, and embeddings
• Deep experience in data modeling, ETL, schema design, and modern data stacks
• Proficiency with frameworks such as PyTorch, TensorFlow, scikit-learn
• Experience with MLOps tooling (Weights & Biases, MLflow, Vertex AI, SageMaker)
• Strong communication skills
• Experience working directly with clients
• Experience in environments that value autonomy, speed, and execution
• High technical curiosity

Required Skills

• Machine Learning
• Data Engineering
• ML Model Design
• ML Model Training
• ML Model Deployment
• Data Infrastructure
• Data Pipelines
• Schema Models
• ETL Processes
• Semantic Layers
• Knowledge Graphs
• Feature Stores
• ML Ops
• Model Versioning
• Model Monitoring
• Model Retraining
• Model Interpretability
• Supervised Learning
• Unsupervised Learning
• Optimization
• Evaluation
• Feature Engineering
• Clustering
• Embeddings
• Data Modeling
• Modern Data Stacks
• PyTorch
• TensorFlow
• scikit-learn
• MLOps Tooling
• Weights & Biases
• MLflow
• Vertex AI
• SageMaker
• Communication Skills
• Systems Thinking
• Technical Curiosity

Team members: CEO, CTO, Founding engineers

Salary: 90,000 - 100,000 USD

Equity: 0.5%-1%, Founding ownership: Equity, autonomy, and the chance to shape our machine learning foundation.

Seniority level: Mid-Senior level

Employment type: Full-time

Job function: Engineering and Information Technology

Industries: Software Development