Job Specifications
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: 90000 - 130000 USD
Equity: Equity, 0.5%-1%, Founding ownership: Equity, autonomy, and the chance to shape our machine learning foundation.