Machine Learning Engineer

Atomicmaps; United States; On-site; USD 90,000 - 130,000; Full time

Job summary

Atomic Maps is looking for a Machine Learning Engineer to lead the full ML lifecycle and enhance geospatial AI systems. This position entails building, deploying, and maintaining scalable ML models, collaborating with infrastructure and data engineering teams. Desired candidates should have strong Python skills, production ML experience, and familiarity with MLOps tools. Join a supportive startup to deepen your ML skills and tackle technical challenges in a fast-paced environment.

Qualifications

• Strong production ML experience and solid ML fundamentals.
• Experience with deploying ML models for imagery and video data.
• Strong problem-solving and communication skills.

Responsibilities

• Design and implement scalable, reproducible ML pipelines.
• Build automated feedback and retraining loops.
• Own the ML model lifecycle from training to deployment.
• Develop and deploy containerized inference services.
• Collaborate with product teams for integration and impact.

Skills

Python

Production-grade code

MLOps tools (MLflow, Kubeflow)

Machine Learning frameworks (PyTorch, TensorFlow)

Geospatial workflows

Tools

Docker

Argo Workflows

Airflow

Terraform

Job description

Company: Atomic Maps; [e-mail]

The Role

We're seeking a Machine Learning Engineer with strong production ML experience to own the full ML lifecycle, bring models to production, and help us scale our geospatial AI systems. This is a hands‑on technical role where you'll build, deploy, monitor, and maintain models in production. You will deploy clean, testable, containerized, and scalable inference workflows to Atomic Flow that serve our customers across imagery, video, and 3D data.

You will work closely with the infrastructure and data engineering teams with a focus on operationalizing our ML and MLOps systems and ensuring they run reliably and effectively in production. This collaborative setup lets you focus on what you do best: developing robust, well‑engineered ML systems, fine‑tuned for customer use cases, and ensuring they perform reliably at scale.

It's an ideal opportunity for a mid‑level engineer with solid ML fundamentals who wants to deepen their production ML skills and gain hands‑on MLOps experience in a supportive, fast‑moving startup environment.

Responsibilities

Design and implement scalable, reproducible ML pipelines for training, retraining, and deploying models

Build automated feedback and retraining loops that incorporate labeled data from imagery and other modalities

Own the model lifecycle from training and validation through deployment, monitoring, and retraining using MLflow, Kubeflow, or similar tools

Develop and deploy containerized inference services for large‑scale geospatial and computer vision workloads

Collaborate with data engineering, infrastructure, and product teams to ensure smooth integration and measurable impact

Stay current with advances in ML and evaluate practical opportunities for adoption

Key Skills & Experience

Required

Strong Python skills with experience in writing clean, maintainable, and testable production‑grade code with libraries like PyTorch, TensorFlow, etc.

Experience building, fine‑tuning, and deploying ML models for imagery and video data (e.g., detection, segmentation, or feature extraction) using YOLO, SAM, etc.

Experience with one or more MLOps tools (MLflow, Kubeflow, or similar) for experiment tracking, deployment, and monitoring

Experience writing production‑grade code (modular packages, reusable libraries, and automated testing, CI/CD integration) for ML pipelines and models

Excellent problem‑solving and communication

Nice to Have

Experience with geospatial or computer vision workflows

Solid understanding of data structures, SQL, and cloud storage patterns

Familiarity with cloud platforms (AWS, GCP, or Azure) and GPU‑based training environments

Experience with containerized workflows using Docker

Experience with orchestration platforms like Argo Workflows, Airflow, or Prefect

Familiarity with Infrastructure as Code tools like Terraform

Experience with OpenSearch or ElasticSearch

Why Join Us

You’re passionate about building ML systems that last beyond the first deployment

You enjoy tackling hard technical problems in a collaborative environment

You’re curious and eager to experiment and pick up the latest technologies and foundational models as they are released

You are ready to help shape our ML engineering practices, improving how we build, deploy, and maintain models in production

Atomic Maps is an equal opportunity employer. We do not discriminate on the basis of race, religion, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.