Machine Learning Engineer

Reposted 19 Days Ago
2 Locations
In-Office
Mid level
Artificial Intelligence • Information Technology • Software
The Role
The Machine Learning Engineer will develop and innovate ML models and their infrastructure, collaborate, mentor, and take ownership of impactful projects.
Summary Generated by Built In
Role Description

We are seeking an experienced machine learning engineer to join our seasoned founding team to drive the development and innovation of our ML platform. Ideal candidates bring extensive experience in building the next generation of machine learning models and its training and serving infrastructure for the Spector.

This role requires a hands-on engineer who is passionate about our mission, thrives in a startup environment, and is committed to pushing the boundaries of what ML and GenAI can achieve in industrial resilience.

You will:
  • Create ML models which help drive value for users and our company.

  • Build scalable ML 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.

Must Have:
  • Bachelor's Degree in a relevant technical field such as computer science and 3+ years of post-Bachelor’s machine learning experience; or Master’s degree in a technical field + 2+ year of post-grad machine learning experience; or PhD in a relevant technical field + 1 years of post-grad machine learning experience.

  • Experience developing machine learning models for supervised, unsupervised, ranking, or other relevant applications of machine learning.

  • 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 both internal and external partners.

  • Strong collaboration and mentorship skills.

  • Entrepreneurial Mindset: Highly self motivated and adaptable 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.

Preferred to have:
  • Experience in on-prem ML model deployment and observability.

  • Experience in building and deploying ML models for real time time series data like sensor/IoT data.

About Spector.ai

Spector.ai is a well-funded, fast-paced, innovative seed-stage startup focused on a mission to solve the $1.5 trillion challenge of industrial asset reliability. Spector is building an AI-first industrial agent platform designed to transform plant reliability and performance from reactive to autonomous operations. By combining machine learning and domain-specific industrial AI Agents, Spector.ai enables real-time diagnostics, root cause analysis, and actionable recommendations at scale. The platform extracts insights from complex industrial data including unstructured documentation and live sensor streams reducing false positives, shortening time to resolution, and scaling expertise without reliance on data scientists.

We are rapidly growing and looking for an experienced, self-driven DevOps Engineer to join our core team. This is a unique opportunity to shape our infrastructure, tooling, and deployment practices from the ground up, ensuring we can scale effectively and reliably as we move toward our next stage of funding and growth.

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The Company
18 Employees
Year Founded: 2023

What We Do

Spector.ai is an AI Agent-powered platform that streamlines the onboarding and ongoing management of industrial plant performance and reliability. Our AI Agents accelerate and simplify traditionally time-intensive tasks across the reliability lifecycle—from extracting plant data for supervised ML training and failure mode identification, to assisting operators with diagnostics, root cause analysis, and actionable recommendations. By continuously learning from new events and optimizing models in real time, Spector.ai enables plants to maximize reliability, performance, and uptime.

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