ML / Data Platform Engineer - World Models

Deca Talent; San Francisco (CA); On-site; USD 180,000 - 240,000; Full time

Job summary

Deca Talent is seeking a Lead ML & Data Platform Engineer to own the data processing pipelines and ML platform for next-generation World Models. You will design scalable data and ML infrastructure, build pipelines for ingesting and serving vast datasets, and collaborate with ML researchers to accelerate model training and evaluation in cloud and GPU-heavy environments.

Candidates should have strong software engineering fundamentals, experience with ML infrastructure, Python or Go, data

Qualifications

• Strong software engineering fundamentals for production infra.
• Experience with ML infrastructure or large-scale distributed systems.
• Experience with Python and/or Go.
• Experience building data pipelines and working with large datasets.

Responsibilities

• Design and build scalable data and ML infrastructure for large-scale model development.
• Build reliable pipelines for ingesting, processing, transforming, and serving data.
• Develop infrastructure linking datasets, training runs, model versions, experiments, and inference.
• Track data lineage, metadata, and ML artifacts.
• Collaborate with ML researchers to improve training speed and reproducibility.
• Develop distributed systems across cloud, clusters, and GPUs.
• Support architectural decisions as ML infra scales.
• Identify bottlenecks and implement pragmatic solutions.

Skills

Software engineering

ML infrastructure

Data pipelines

Cloud computing

Kubernetes

Distributed systems

Tools

Python

Go

Kubernetes

Cloud platforms

Job description

Lead ML & Data Platform Engineer - Physical AI / World Models

Deca Talent are working with a fast growing AI company building a new intelligence layer for the physical world, who have recently raised $50M to advance this mission. They are developing next-generation technology that captures rich data about the physical world, and combining cutting edge ML to create World Models which are accurate at the molecular level.

As this is a data heavy mission, they are looking for a Lead ML/Data Platform Engineer to own their data processing pipelines and ML Platform which supports the building of these Models/ Digital Twins.

What you’ll do

• Design and build scalable data and ML infrastructure supporting large-scale model development and experimentation
• Build reliable pipelines for ingesting, processing, transforming, and serving large volumes of data
• Develop infrastructure connecting datasets, training runs, model versions, experiments, and downstream inference
• Build systems for tracking and managing data lineage, metadata, and ML artifacts
• Work closely with ML researchers and engineers to improve the speed, reliability, and reproducibility of model training
• Develop distributed systems that operate across cloud infrastructure, compute clusters, and GPU-heavy workloads
• Help establish the foundations for large-scale experimentation and AI model evaluation
• Identify bottlenecks across the data and ML lifecycle and build pragmatic solutions to remove them
• Contribute to architectural decisions as the company scales its ML infrastructure

What we’re looking for

• Strong software engineering fundamentals with experience building production infrastructure
• Experience with ML infrastructure, data platforms, or large-scale distributed systems
• Strong Python and/or Go experience
• Experience building data pipelines and working with large datasets
• Experience with cloud infrastructure and modern containerized environments
• Familiarity with distributed compute, Kubernetes, GPUs, or high-performance computing is highly valuable
• Understanding of machine learning workflows and the infrastructure required to train and evaluate models
• Strong ownership mindset and ability to operate effectively in an early-stage environment
• Comfortable working closely with ML researchers and engineers to solve ambiguous infrastructure problems

Nice to have

• Experience with ML platforms, model training infrastructure, or experiment management
• Experience with GPU infrastructure or distributed training
• Experience with simulation, reinforcement learning, or synthetic data generation
• Experience building systems for large-scale data collection or processing
• Experience with observability, telemetry, or ML/data lineage
• Experience working on infrastructure for computer vision, robotics, autonomy, or other real-world AI applications

If you’re excited by the intersection of ML, data infrastructure, distributed systems, and real-world AI,