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Spector.ai

Industrial AI platform for plant reliability

Machine Learning Engineer

Applied Machine LearningConfirmed live in the last 24 hours
Full-timeBengaluru, Karnataka, IndiaNo salary listedHybrid

About the Job

Requirements

  • A bachelor's degree in a relevant technical field such as Computer Science and at least 3 years of post-bachelor's machine learning experience; or a master's degree in a technical field and at least 2 years of post-graduate machine learning experience; or a PhD in a relevant technical field and at least 1 year of post-graduate machine learning experience.
  • Experience developing machine learning models for supervised, unsupervised, ranking, or other relevant machine learning applications.
  • A strong understanding of machine learning approaches and algorithms.
  • Experience working with machine learning frameworks such as TensorFlow, PyTorch, Spark ML, scikit-learn, or related frameworks.
  • Ability to work with internal and external partners.
  • Strong collaboration and mentorship skills.
  • A self-motivated and adaptable entrepreneurial mindset with a passion for innovation, problem-solving, and making a meaningful impact in a startup environment.
  • Willingness to take ownership and drive projects from concept to implementation.

Responsibilities

  • Create machine learning models that drive value for users and the company.
  • Build scalable machine learning model training and serving infrastructure.
  • Evaluate the technical tradeoffs of every decision.
  • Perform code reviews and ensure exceptional code quality.
  • Iterate quickly without compromising quality.

Desired Qualifications

  • Experience in on-premises machine learning model deployment and observability.
  • Experience building and deploying machine learning models for real-time time-series data such as sensor or Internet of Things data.

About the Company

Spector.ai

Industrial AI platform for plant reliability

Spector provides an Agentic Twin platform for plant reliability and performance in industrial settings. AI agents learn how assets behave and continuously monitor equipment, delivering diagnostics and recommendations that operators can trust, with deployment available in weeks in the cloud or on-premises. It covers Asset Reliability, Process Reliability, Performance Optimization, and Plant Analysis and Troubleshooting, using a unified foundation to monitor asset health and optimize operations across sectors like oil and gas, chemicals, power, and manufacturing. The goal is to put plant reliability and performance on autopilot, helping operators maintain high uptime as experienced staff retire.

Company size
11-50
Company stage
Early VC
Total funding
$6.7M
Headquarters
San Jose, CA
Founded
2023

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