Full Stack Machine Learning Engineer

Pieces is looking for a Full Stack Machine Learning Engineer ready to work with new technologies and architectures, and collaborate on difficult problems with a forward-thinking team that’s always pushing boundaries. ML Engineers at Pieces are motivated to build state-of-the-art, first-of-a-kind Natural and Technical Language Processing models (NLP, TLP) and Computer Vision models (CV) to enrich file fragments with useful metadata. This role will generally design, implement, and enhance ML software inevitably directly impacting the end consumer experience.

Our ideal candidate has experience building production ready products across the stack with a firm understanding of APIs, data structures, statistics, and multiple back end languages.

Pieces is a venture-backed 25 person startup with a first-of-its-kind productivity platform that enables professional creators - developers and designers initially - to save, store and share code snippets, links, raw text, screenshots, images, design layers and more with a simple copy and paste or right-click.  Pieces is headquartered in the OTR neighborhood in Cincinnati, Ohio, with team members in San Francisco, Canada, Ireland, Spain, Poland and India.

This role can be based anywhere in the US, with a preference for Cincinnati, OH.

Learn more at https://pieces.app

What you'll do:

  • Efficiently create, analyze and manipulate large data lakes
  • Perform exploratory and targeted data analysis using analytical statistics methods
  • Implement and adapt machine learning algorithms and tools from the latest research
  • Design and implement innovative deep learning algorithms to solve challenging problems
  • Deploy, monitor, and support data-driven models in production

What we're looking for:

Must-Haves:

  • Proficiency with at least one machine learning framework, such as Tensorflow, PyTorch or ONNX
  • Experience with data mining, analysis, and visualization
  • Ability to understand hardware/software implications & constraints when designing, training, and serving an ML model, additionally considering runtime, latency, bandwidth, and memory
  • Strong foundation in computer science fundamentals including data structures, algorithms, recurrence relations, Big-O notations and Memory vs Time Complexity Analysis
  • A passion for code quality 🙂
  • A real passion for making simple, robust, and scalable platforms used by other developers
  • Excellent problem solving, debugging skills, optimism, and a sense of ownership + accountability
  • Strong communication skills, a "can-do", empathetic, and team-oriented attitude
  • Ability to dynamically lead, follow and collaborate
  • A naturally curious mindset with the confidence to learn new software and technologies quickly
  • Self ­awareness and ambition with a desire to improve your skills
  • Strong organizational skills and attention to detail
  • 2+ years experience in machine learning

Nice-to-Haves:

  • Experience with edge device training and inference software, such as Tensorflow Lite
  • Experience with Google Cloud Platform
  • Experience with an ML orchestration software, such as Kubeflow or Vertex Pipelines
  • Experience working at a startup

The above statements are intended to describe the general nature and level of work being performed by people assigned to this position.  The requirements listed above are representative of the minimum knowledge, skill, and/or ability required.  To perform this job successfully, an individual must be able to satisfactorily perform the essential functions of the job according to specific company requirements.  Reasonable accommodations will be made to enable individuals with disabilities to perform the essential functions.

Compensation

We offer competitive salaries and benefits at Pieces, including medical, vision & dental insurance and a flexible PTO policy.

Employment Type

Full-Time

Mesh Intelligent Technologies, Inc. (d.b.a Pieces) is an Equal Opportunity Employer. All qualified applicants will be considered for employment without regard to ethnicity, color, national origin, age, religion, sexual orientation, gender identity or expression, family or parental status, veteran status, neurodiversity status, disability status, or any other basis protected by applicable law.

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