Senior Machine Learning Engineer (Remote - US)

This position is posted by Jobgether on behalf of Caylent. We are currently looking for a Senior Machine Learning Engineer in the United States. We are seeking a Senior Machine Learning Engineer to design, implement, and deliver AI and ML solutions on cloud platforms for enterprise clients. This role combines deep technical expertise in machine learning, MLOps, and big data processing with hands-on experience in AWS services. You will work closely with cross-functional teams to translate business objectives into scalable, high-performance models and analytics solutions. The position offers a fully remote, collaborative environment where innovation, technical curiosity, and continuous learning are highly valued. You will have the opportunity to shape and deploy solutions that directly impact client outcomes while advancing your own expertise in cutting-edge ML technologies. Accountabilities ML Solution Development – Design, develop, and implement machine learning models and pipelines to address real-world business problems. Cloud & MLOps – Deploy, manage, and optimize ML workflows using AWS services, MLOps frameworks, and infrastructure-as-code tools. Big Data Processing – Prepare, clean, and process large datasets using Python, Pandas, PySpark, SQL, or other big data frameworks. Collaboration & Agile Delivery – Participate in agile ceremonies, daily standups, and cross-functional team discussions to align technical solutions with client goals. Model Optimization & Feature Engineering – Define features, tune hyperparameters, and apply best practices for stochastic gradient descent, deep learning architectures, and activation functions. Visualization & Reporting – Create visualizations and insights using SageMaker, matplotlib, seaborn, or other tools to communicate model results and performance. Requirements Strong experience building ML models for real-world applications, with proficiency in AWS ML services (SageMaker) or libraries such as TensorFlow, PyTorch, Keras, or Scikit-learn. Experience with MLOps tools such as MLflow, Kubeflow, or Airflow. Proficiency in big data platforms (Spark, Hadoop) and data processing in Python, PySpark, Scala, or SQL. Strong understanding of feature definition, model metadata, hyperparameter tuning, and deep learning concepts. Experience with visualization libraries and cloud IaC tools like CloudFormation, Amazon CDK, or Terraform. Excellent written and verbal communication skills, with the ability to collaborate across distributed teams. Preferred: experience with streaming platforms, advanced analytics, and implementing ML in production environments. Benefits Fully remote work with global team collaboration. Competitive base salary: $120,000 – $135,000 per year, plus potential bonuses, equity, and other incentives. Medical, dental, and vision insurance for you and eligible dependents. 401(k) plan with company match up to 4% and immediate vesting. Term life and disability insurance coverage. Flexible Spending Accounts for healthcare and dependent care. Annual learning and development stipend. Company-issued laptop and equipment stipend. Unlimited paid time off (after 90-day probation) and 10 paid holidays. Up to 25% travel depending on project needs. Jobgether is a Talent Matching Platform that partners with companies worldwide to efficiently connect top talent with the right opportunities through AI-driven job matching. When you apply, your profile goes through our AI-powered screening process designed to identify top talent efficiently and fairly. Our AI evaluates your CV and LinkedIn profile thoroughly, analyzing your skills, experience, and achievements. It compares your profile to the job’s core requirements and past success factors to determine your match score. Based on this analysis, we automatically shortlist the 3 candidates with the highest match to the role. When necessary, our human team may perform an additional manual review to ensure no strong profile is missed. The process is transparent, skills-based, and free of bias — focusing solely on your fit for the role. Once the shortlist is completed, we share it directly with the company that owns the job opening. The final decision and next steps (such as interviews or additional assessments) are then made by their internal hiring team. Thank you for your interest! Originally posted on Himalayas