Summary
 

We are seeking a Senior Machine Learning Engineer to join our fast-paced and mission-driven team. In this role, you’ll take full ownership of delivering production-grade ML features that provide real value to world-class hardware manufacturers. You’ll collaborate closely with engineers, product managers, and data teams to build highly scalable ML systems,  including deep learning and LLM-based approaches, that operate reliably in real-world manufacturing environments.

 

This is a hands-on position for someone who thrives in cross-functional teams, is passionate about using ML to solve practical problems, and is eager to build robust pipelines that go from raw data to actionable insights for our customers.

 

Salary: $90k–115k/year 
Remote: LATAM

Key Responsibilities

 

• Design, build, and maintain scalable ML systems from end to end, from data collection and training to deployment and monitoring.

• Deliver ML features that integrate seamlessly into customer-facing products and drive measurable outcomes.

• Rapidly prototype new ML approaches using deep learning, computer vision, and LLMs, then prioritize based on customer impact.

• Collaborate across engineering, product, and data teams in a highly integrated and supportive environment.

• Guide the collection and curation of high-quality training datasets.

• Manage and improve the ML pipeline, including resource scheduling, model versioning, and performance optimization.

• Communicate complex ML concepts clearly across technical and non-technical stakeholders.

Required Skills

 

• Proven experience deploying ML systems at scale in production environments.

• 3+ years of experience coding in Python, Java, or Scala.

• Experience with deep learning frameworks (e.g., TensorFlow, PyTorch) in real-world applications.

• Solid understanding of the full ML lifecycle: data pipelines, training, evaluation, deployment, and monitoring.

• Strong communication skills and ability to work across teams.

• Familiarity with best practices in software engineering: testing, version control, and code quality.

Nice-to-have Skills

 

• Hands-on experience with LLMs, multimodal models, or computer vision systems.

• Knowledge of model optimization for performance (e.g., quantization, batching, caching).

• Experience working in high-ownership environments such as startups or fast-paced teams.

• Familiarity with modern ML infrastructure tools and MLOps practices.

• Background or exposure to electronics manufacturing or industrial processes.

• Experience with data annotation workflows and dataset management at scale.