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Machine Learning Operations Engineer

Nikola Labs Inc.

Nikola Labs Inc.

Software Engineering, Operations
Remote · United States
Posted on Thursday, August 3, 2023

AssetWatch serves global manufacturers by powering manufacturing uptime through the delivery of an unparalleled condition monitoring experience, with a passion to care about the assets our customers care for every day. We are a devoted and capable team that includes world-renowned engineers and distinguished business leaders united by a common goal – To build the future of predictive maintenance. As we enter the next phase of rapid growth, we are seeking people to help lead the journey.

We're seeking an experienced MLOps Engineer to join our team and play a critical role in developing, deploying, and managing machine learning models on AWS. You will collaborate with cross-functional teams to implement end-to-end ML pipelines, ensure seamless model deployment, and continuously monitor and evaluate model performance. As a subject matter expert in MLOps, you will also mentor other engineers and contribute to the development of our company's ML engineering capabilities.

What You'll Do:

  • Infrastructure & Pipeline Management: Set up optimal ML infrastructure on AWS and construct a robust pipeline, covering data preprocessing, model training, and tuning.
  • Data Ingestion & Feature Management: Ensure efficient data ingestion mechanisms and design Feature Store Data Models for streamlined storage of engineered features.
  • AI Model Deployment on AWS: Seamlessly transition trained models into AWS production environments, ensuring integration and performance.
  • Automation & Scaling: Streamline the model training process with automation, ensuring scalability and adaptability.
  • Inference Pipelines: Craft inference mechanisms post-training that prioritize client load balancing and utilize containerization.
  • Security & Version Control: Implement top-tier security standards, especially during deployment, and maintain best practices for ML model and data versioning.
  • Model Monitoring & Evaluation: Establish mechanisms for data drift or concept drift post-deployment and initiate A/B tests to guide model refinements.
  • Team Engagement & Continuous Learning: Collaborate with interdisciplinary teams throughout the model's lifecycle and stay updated on MLOps trends, AWS, and machine learning innovations.

Who You Are:

  • BS or MS in Computer Science, Computer Engineering, or related field.
  • 5+ years of industrial experience in ML and with AWS.
  • Demonstrable experience in deploying and prototyping AI models on AWS.
  • Hands-on experience with specific AWS tools including AWS/Amazon SageMaker, ECS, Lambda, etc.
  • Proficiency with Feature Store Data Models and database management systems, particularly those optimized for ML workloads.
  • Strong proficiency in programming languages such as Python and SQL.
  • Deep understanding of containerization techniques, especially in the context of ML model inference.

Bonus Points For:

  • Familiarity with LLMs, Vector Databases and integration tools such as LangChain
  • Understanding of a deep learning framework such as TensorFlow/Keras, or PyTorch and HuggingFace for LLMs
  • Familiarity with AI Model Explainability and Interpretability techniques
  • Experience with cloud-based services for IIoT
  • Familiarity with condition monitoring and predictive maintenance methodologies.
  • Experience with big data processing frameworks like Apache Spark or Hadoop.

What We Offer:

AssetWatch is a remote-first rapidly growing startup providing a game changing condition monitoring platform and mobile experience in the industrial manufacturing space.

  • Competitive compensation package including share options.
  • Flexible work schedule
  • Full benefits and 401K
  • Opportunity to make a real impact every day
  • Opportunity to work with an exciting and growing team
  • Unlimited PTO

We have a distributed team that works remotely across locations in the United States. We are open to candidates from most states but collaboration within core working hours is required.