ML Engineer

Compunnel

$100K — $120K *
Information Technology
Less than 5 years of experience
Job Overview by Ladders

Qualifications

  • Bachelor's degree in Computer Science, Data Science, or related field
  • Experience with machine learning frameworks and libraries
  • Familiarity with data wrangling and preprocessing techniques
  • Understanding of model evaluation metrics
  • Ability to work collaboratively in cross-functional teams
  • Strong documentation skills for communicating technical concepts

Responsibilities

  • Execute data wrangling to prepare datasets for modeling
  • Conduct experiments to assess feasibility and establish baseline models
  • Optimize baseline models for enhanced performance
  • Test models against business acceptance criteria
  • Document artifacts for effective communication
  • Collaborate with data scientists for model deployment
  • Engage with product teams to support new product releases
  • Define OKRs and provide feedback for team performance

Benefits

  • Professional development opportunities and mentorship
  • Exposure to state-of-the-art machine learning techniques
  • Collaborative work environment with cross-functional teams
  • Opportunity to contribute to impactful business solutions
  • Flexible work schedule and potential for remote work
Full Job Description
ML Engineer I
JOB SUMMARY
Under guidance from Senior ML Engineers, develop ML models that provides accurate results with controls to solve the business problem identified, using state of art techniques.
Key Responsibilities
1. Executes relevant data wrangling activities related to the problem in order to create dataset
2. Conduct ML experiments to understand the feasibility and build baseline models to solve the business problem
3. Fine tune the baseline model for optimum performance
4. Test Models internally per acceptance criteria from business
5. Document relevant Artefacts for communicating with the business
6. Work with data scientists to deploy the models.
7. Work with product teams in planning and execution of new product releases.
8. Set OKRs and success steps for self/ team and provide feedback to goals for team members
9. Work with cross functional teams - business, technology, and product teams to understand the product vision; building ML solutions that provides value to the product

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