Remote Tecton Jobs

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Software Engineer, Foundations
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Software Engineer, Foundations
30+ days ago

Tecton

76% flex
Matchscore
  •  Full time
  • Remote: United States
Software Engineer, Collaboration
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Software Engineer, Collaboration
30+ days ago

Tecton

76% flex
Matchscore
  •  Full time
  • Remote: United States
Enterprise Product Manager
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Enterprise Product Manager
30+ days ago

Tecton

76% flex
Matchscore
  •  Full time
  • Remote: United States
Solutions Architect, PST/MST
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Solutions Architect, PST/MST
30+ days ago

Tecton

79% flex
Matchscore
  •  Full time
  • Remote: Arizona (USA), California (USA), Colorado (USA), Indiana (USA), Maryland (USA)
Technical Documentation Lead
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Technical Documentation Lead
19 days ago

Tecton

79% flex
Matchscore
  •  Full time
  • Remote: United States, Canada, California (USA), New York (USA)
Engineering Manager, Feature Engineering
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Engineering Manager, Feature Engineering
30+ days ago

Tecton

76% flex
Matchscore
  •  Full time
  • Remote: New York (USA)

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Discover more about Tecton ?

Founded by the team that created the Uber Michelangelo platform, Tecton provides an enterprise-ready feature platform to make world-class machine learning accessible to every company. Machine learning creates new opportunities to generate more value than ever before from data. Companies can now build ML-driven applications to automate decisions at machine speed, deliver magical customer experiences, and re-invent business processes. But ML models will only ever be as good as the data that is fed to them. Today, it’s incredibly hard to build and manage ML data. Most companies don’t have access to the advanced ML data infrastructure that is used by the internet giants. So ML teams spend the majority of their time building custom features and bespoke data pipelines, and most models never make it to production. We believe that companies need a new kind of data platform built for the unique requirements of ML. Our goal is to enable ML teams to build great features, serve them to production quickly and reliably, and do it at scale. By getting the data layer for ML right, companies can get better models to production faster to drive real business outcomes.