Active Learning – ML Engineer

Blue Signal Search; United States; On-site; USD 120,000 - 190,000; Full time

Benefits offered by this job

Equity participation

Remote work flexibility

Ownership of data platform

Job summary

Blue Signal Search is seeking an Active Learning ML Engineer to build scalable pipelines that convert large volumes of raw video into dependable training data for computer vision models. This role offers remote work nationwide with EST/CST hours, and significant ownership over a core ML data platform.

You will work across software, data, and ML, applying state-of-the-art foundation models and modern cloud tools to accelerate labeling, validation, and model improvement while maintaining high

Qualifications

• Production ML infrastructure experience
• Strong Python and software engineering discipline
• Experience with data pipelines and ML training data pipelines

Responsibilities

• Build scalable pipelines transforming raw video into organized training datasets
• Create active learning and weak supervision workflows for labeling and review
• Apply foundation models (e.g., Grounding DINO, SAM 2) to accelerate labeling
• Develop internal apps for annotator validation and workflow automation
• Own data quality across cloud ingestion, cleaning, organization and delivery of training assets
• Establish evaluation datasets and quality controls for ML performance

Skills

Python

FFmpeg

SQL

Computer vision

Active learning

Foundation models

AWS/GCP

PyTorch/TensorFlow

Web tooling

Tools

React

Next.js

FastAPI

Flask

SageMaker

Job description

Location: Remote, Nationwide, EST and CST

Our client is an early-stage technology company building sophisticated computer vision systems where the quality of the underlying data is central to product performance. They are seeking an Active Learning- ML Engineer to build the infrastructure that transforms large volumes of raw video into dependable training data at scale. This is a high-ownership opportunity for an engineer who enjoys working across software, data, and applied ML, and who wants to create the systems that continuously improve how computer vision models learn.

This Role Offers:

• Competitive compensation with meaningful equity participation.
• Remote flexibility for candidates located nationwide and able to work primarily within EST or CST hours.
• Significant ownership over the architecture and evolution of a core machine learning data platform.
• Hands-on exposure to modern computer vision, active learning, foundation models, cloud infrastructure, and AI-assisted software development.
• A builder-focused culture that values rapid experimentation without sacrificing reliability, maintainability, or engineering quality.

Focus:

• Build scalable pipelines that transform high volumes of raw video into organized, version-controlled datasets for computer vision development.
• Create active learning and weak supervision workflows that combine automated annotation, tracking, geometric reasoning, and targeted expert review.
• Apply state-of-the-art foundation models, including approaches that combine Grounding DINO and SAM 2, to accelerate labeling and solve complex vision challenges.
• Develop internal applications that enable experts to efficiently inspect predictions, validate annotations, and resolve difficult cases.
• Own data quality throughout the lifecycle, including cloud ingestion, cleaning, organization, validation, and delivery of training-ready assets.
• Establish evaluation datasets and quality controls that expose difficult failure cases and provide reliable measures of ML performance.
• Build dependable production services with strong standards for testing, observability, maintainability, code quality, and system availability.
• Leverage AI coding agents to rapidly develop internal tools, interfaces, automation, and engineering workflows while maintaining production-quality standards.

Skill Set:

• Strong professional experience building production software, ML infrastructure, computer vision systems, data platforms, or related technology.
• Hybrid software engineering and data mindset, with equal attention to code quality, unit testing, system uptime, dataset integrity, and ML performance.
• Strong Python development skills plus experience with FFmpeg, SQL, and production-quality software engineering practices.
• Hands-on computer vision expertise involving object detection, multi-object tracking, video analysis, camera geometry, homography, or calibration.
• Experience with active learning, automated or programmatic labeling, weak supervision, and human-in-the-loop data workflows.
• Strong foundation-model literacy, including the ability to chain state-of-the-art models such as Grounding DINO and SAM 2 rather than defaulting to building models from scratch.
• Expert use of AI coding agents to rapidly develop internal applications and tooling, with working knowledge of web technologies such as React, Next.js, FastAPI, or Flask.
• Experience with AWS or GCP technologies such as S3, Lambda, SageMaker, or Vertex AI, along with familiarity with PyTorch, TensorFlow, or comparable ML frameworks.

Preferred Experience:

• Background building data engines, annotation platforms, computer vision infrastructure, or ML tooling at a technology company.
• Experience developing human-in-the-loop systems that combine automated predictions with efficient expert validation.
• Exposure to multimodal machine learning or large-scale video processing.
• Experience working in an early-stage company or another environment requiring significant individual ownership.
• Interest in solving difficult spatial and temporal computer vision problems.