Senior Machine Learning Engineer

Job Title: Machine Learning Advisor

Location: RemotePosition Type: Part-Time (20 hours/week)Experience Level: Senior (8 to 10+ years)

About the Role:

hands-on Machine Learning Advisor

• Building and validating the

  first versions of our models

  .
• Setting up the right processes, frameworks, and best practices.
• Working closely with junior team members to

  fine-tune, scale, and optimize

   models.

builder + advisor hybrid capacity

Key Responsibilities

• Hands-On Model Development

• Design and build the

  initial versions of ML models

   (regression, classification, clustering, or deep learning, depending on problem type).
• Perform feature engineering, experimentation, and hyperparameter tuning.
• Develop clean, scalable code for model training and inference.

• Mentorship & Refinement

• Work with junior ML engineers to

  iterate, refine, and fine-tune

   models.
• Review their code, guide debugging, and introduce advanced techniques.
• Transfer knowledge and upskill the team for long-term ownership.

• Architecture & Best Practices

• Define the

  ML project workflow

   (from data prep to deployment).
• Set up reproducibility, versioning, and experiment tracking.
• Recommend scalable approaches for data pipelines and model serving.

• Collaboration

• Partner with product and engineering to align ML solutions with business needs.
• Communicate model performance, limitations, and trade-offs clearly to both technical and non-technical stakeholders.
• 8 to 10+ years of professional experience in machine learning, including

  building and deploying models

  .
• Strong proficiency in

  Python (Pandas, NumPy, Scikit-learn)

   and ML frameworks (

  TensorFlow, PyTorch, or Keras

  ).
• Experience designing

  end-to-end ML workflows

  , from feature extraction to deployment.
• Solid knowledge of ML algorithms, evaluation metrics, and optimization techniques.
• Hands-on experience with SQL and big data tools (Spark, PySpark).
• Familiarity with ML lifecycle tools (MLflow, Weights & Biases, DVC, Git).
• Strong track record of

  mentorship and technical leadership

  .

Preferred Qualifications (Good to Have)

• Experience with cloud ML platforms (AWS SageMaker, GCP Vertex, Azure ML).
• Exposure to deploying ML models as APIs (FastAPI, Flask).
• Familiarity with containerization (Docker, Kubernetes).
• Contributions to open-source ML projects or publications.

What We Offer

• A

  part-time, flexible role

   with high impact (20 hours/week).
• The opportunity to

  both build and mentor

    shaping the foundation of our ML team.
• A collaborative environment with motivated engineers eager to learn.
• Competitive compensation tailored for senior part-time talent.

  Role & responsibilities

Preferred candidate profile: Who are interested in Part time to handle a team and who can join in a week notice.