Machine Learning Engineer

Our client is looking for a Machine Learning Engineer.

• Data Engineering & Pipeline Support:
• Assist in designing and maintaining data pipelines from various sources.
• Build, cleanse, and audit pipelines to ensure high-quality data outputs.
• Collaborate on developing robust data infrastructure and workflows.
• Machine Learning & Data Science:
• Support the building, deployment, and monitoring of machine learning models.
• Participate in data processing, feature selection, and model testing.
• Stay updated with AI/ML advancements and contribute to their implementation.
• Collaboration & Knowledge Sharing:
• Work closely with product, engineering, and business teams to align on product features.
• Contribute to cross-functional processes to integrate data science into product development.
• Product & Business Insights:
• Assist in developing user metrics and KPIs to improve engagement.
• Help build and maintain analytics dashboards that provide actionable insights.
• Growth & Learning:
• Actively seek opportunities to develop your skills and contribute fresh ideas to the team.
  

• Emerging Professional:
• You have a few years of experience in data science or machine learning, or you’re a recent graduate with relevant projects.
• Technically Proficient:
• Familiarity with Python, R, or Scala.
• Some experience with machine learning frameworks like PyTorch or TensorFlow.
• Data Enthusiast:
• Comfortable with SQL and eager to work with data tools such as Spark, Presto, or Hive.
• Good Communicator:
• Able to explain technical concepts to both technical and non-technical stakeholders.
• Agile Mindset:
• Open to working in Agile or Lean environments and incorporating data science into iterative development processes.
• Team Player:
• Ready to collaborate, support the team, and contribute to delivering customer value.
• Lifelong Learner:
• Eager to learn and stay updated with the latest in AI and machine learning.
  

• Job Type: Full-time
• Compensation Range: $140,000 - $150,000
• Location: Remote (U.S. only) with headquarters in Denver, CO