Machine Learning Engineer

Compunnel

$120K — $145K *
Information Technology
Less than 5 years of experience
Job Overview by Ladders

Qualifications

  • 5+ years of experience in Machine Learning or related fields.
  • Proven track record of delivering ML solutions from conception to deployment.
  • Strong understanding of data wrangling and preprocessing techniques.
  • Experience with model evaluation and optimization strategies.
  • Ability to communicate complex ideas to non-technical stakeholders.

Responsibilities

  • Execute data preparation activities to support ML initiatives.
  • Conduct experiments to assess the feasibility of ML models.
  • Refine baseline models for improved performance.
  • Test models against acceptance criteria established by the business.
  • Identify optimization opportunities based on testing outcomes.
  • Document key processes and findings for clear business communication.
  • Collaborate with product teams on new product releases.
  • Establish personal and team OKRs, providing feedback to enhance performance.

Benefits

  • Flexible work hours to promote work-life balance.
  • Collaborative work environment that encourages innovation.
  • Opportunities for professional growth and development.
  • Access to cutting-edge tools and technologies for ML.
  • Engagement in projects that directly impact business outcomes.
Full Job Description
Lead I
JOB SUMMARY
Design and develop ML solutions, that will enable intelligent experiences and provide value. Collaboratively work with business, technology, and product teams to understand the product objectives and formulate the ML problem, under minimal guidance from Lead II.

Key Responsibilities
1. Executes relevant data wrangling activities related to the problem
2. Conduct ML experiments to understand feasibility; building baseline models to solve the business problem
3. Fine tune the baseline model for optimum performance
4. Test Models internally per acceptance criteria from the business
5. Identify areas and techniques to optimize the model based on test results
6. Document relevant artefacts for communicating with the business
7. Work with data scientists to deploy the models.
8. Work with product teams in planning and execution of new product releases.
9. Set OKRs and success steps for self/ team and provide feedback of goals to team members
10. Identify metrics for validating the models and communicate the same in business terms to the product teams.
11. Keep track of the trends and do rapid prototyping to understand the feasibility of utilizing in existing solutions

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