Senior Machine Learning Engineer

Location: Remote

Employment Type: Full-time

About the Role

We are looking for a Senior Machine Learning Engineer to lead the development and deployment of AI/ML models for our platforms. In this role, you will drive technical strategy and you will be responsible for designing and deploying intelligent systems ,mentor junior engineers, and collaborate with cross-functional teams to deliver scalable, production-grade ML solutions.

Key Responsibilities

• Independently design, build, and deploy machine learning models for core use cases.
• Drive the end-to-end lifecycle of ML projects—from scoping and architecture to implementation, deployment, and performance tuning.
• Maintain a hands-on approach in all aspects of development—from data preprocessing and feature engineering to model training, evaluation, and optimization.
• Lead technical reviews, provide constructive feedback, and help grow the team’s skill sets through coaching and knowledge sharing.
• Provide technical leadership and mentorship to junior engineers and data scientists, fostering a collaborative and high-performing team culture.
• Drive ML initiatives from ideation through production, ensuring scalability, performance, and maintainability.
• Collaborate with cross-functional teams including product, engineering, and operations to integrate intelligent solutions into user-facing products.
• Establish and promote ML best practices, including reproducibility, version control, testing,MLOps, and data governance.
• Oversee and guide the creation of scalable and maintainable ML pipelines and infrastructures.
• Stay ahead of industry trends and guide the adoption of new tools and techniques where relevant.
• Evaluate and integrate cutting-edge tools, frameworks, and techniques in NLP, deep learning, and computer vision.
• Own the quality, fairness, and compliance of ML systems, especially in sensitive use cases like content filtering and moderation.
• Design and implement machine learning models for 

  automated content moderation

  , including toxicity, hate speech, spam, and NSFW detection.
• Build and optimize 

  personalized recommendation systems

   using collaborative filtering, content-based, and hybrid approaches.
• Develop and maintain 

  embedding-based similarity search

   for recommending relevant content based on user behavior and content metadata.
• Fine-tune and apply 

  LLMs for moderation and summarization

  , leveraging prompt engineering or adapter-based methods.
• Deploy 

  real-time inference pipelines

   for immediate content filtering and user-personalized suggestions.
• Ensure content moderation models are 

  explainable

  , 

  auditable

  , and 

  bias-mitigated

   to align with ethical AI practices.
• Hands-on experience in 

  content recommendation systems

   (e.g., collaborative filtering, ranking models, embeddings).
• Experience with 

  content moderation frameworks

  , such as Perspective API, OpenAI moderation endpoints, or custom NLP classifiers.
• Strong knowledge of 

  transformer-based models for NLP

  , including experience with Hugging Face, BERT, RoBERTa, etc.
• Practical experience with 

  LLMs (GPT, Claude, Mistral)

   and tools for 

  LLM fine-tuning or prompt engineering

  for moderation tasks.
• Familiarity with 

  vector databases

   (e.g., FAISS, Pinecone) for similarity search in recommendation systems.
• Deep understanding of 

  model fairness, debiasing techniques

  , and 

  AI safety in content moderation

  .

Required Skills & Qualifications

• Bachelor’s or Master’s degree in Computer Science, Machine Learning, Artificial Intelligence, or a related field.
• 5+ years of hands-on experience in machine learning, NLP, or deep learning, with a track record of leading projects.
• Expertise in Python and machine learning frameworks such as TensorFlow, PyTorch, Scikit-learn, Hugging Face, etc.
• Strong background in recommendation systems, content moderation, or ranking algorithms.
• Experience with cloud platforms (AWS/GCP/Azure), distributed computing (Spark), and MLOps tools.
• Proven ability to lead complex ML projects and teams, delivering business value through intelligent systems.
• Excellent communication skills, with the ability to explain complex ML concepts to stakeholders.
• Experience with LLMs (GPT, Claude, Mistral) and fine-tuning for domain-specific tasks.
• Knowledge of reinforcement learning, graph ML, or multimodal systems.
• Previous experience building AI systems for content moderation, personalization, or recommendation in a high-scale platform.
• Strong awareness of ethical AI principles, fairness, bias mitigation, and responsible data usage.
• Contributions to open-source ML projects or published research.