Senior Machine Learning Engineer (Remote - US) - job 1 of 5

This position is posted by Jobgether on behalf of a partner company. We are currently looking for a Senior Machine Learning Engineer in United States.

As a Senior Machine Learning Engineer, you will design, implement, and deploy AI and ML solutions that address complex business challenges. You will work across the full ML lifecycle, from understanding business requirements and analyzing data, to building and optimizing models at scale. The role involves developing end-to-end pipelines, ensuring model performance and reliability, and collaborating with cross-functional teams to integrate ML-driven solutions into production systems. You will also mentor junior engineers, help define best practices, and contribute to the overall AI roadmap. This position demands expertise in modern ML frameworks, cloud platforms, and MLOps practices, with the opportunity to work on cutting-edge technologies in a dynamic, innovative environment. Your contributions will directly impact product development and the efficiency of AI-powered solutions.

Accountabilities:
• Design, implement, and deploy machine learning models, including classification, NLP, recommendation, forecasting, and computer vision systems.
• Build, maintain, and optimize data ingestion, feature engineering, and model training pipelines using frameworks such as TensorFlow, PyTorch, or Scikit-learn.
• Develop and improve MLOps processes, including training, testing, deployment, and monitoring, using tools like Kubernetes, MLflow, Airflow, or SageMaker.
• Analyze model performance, conduct error analyses, and implement improvements for efficiency, latency, and accuracy.
• Collaborate with product, engineering, and data teams to integrate ML-driven solutions into production systems.
• Provide mentorship and guidance to junior engineers and data scientists, shaping best practices and the ML engineering roadmap.

 • Bachelor’s or Master’s degree in Computer Science, Machine Learning, Applied Mathematics, or a related field.
• 5+ years of professional experience in ML system design, development, and deployment.
• Proficiency in Python and ML libraries, including TensorFlow, PyTorch, XGBoost, and Scikit-learn.
• Experience with large-scale models, foundation models, LLMs, or multi-modal AI systems.
• Knowledge of vector databases, retrieval-augmented generation (RAG), embedding pipelines, privacy-preserving ML, federated learning, or reinforcement learning.
• Strong understanding of ML algorithms, neural networks, optimization techniques, data structures, distributed systems, and cloud platforms (AWS, GCP, OCI, or Azure).
• Experience with MLOps tools, including Docker, Kubernetes, AgentCore, or similar platforms.
• Applied statistics knowledge, including distributions, statistical testing, regression, etc.
• Proficiency with source code management and collaboration tools such as Git.
• Strong problem-solving, communication, and collaboration skills in a fast-paced, cross-functional environment.

 • Competitive salary and performance-based incentives.
• Flexible and remote work arrangements.
• Comprehensive healthcare and dental insurance coverage.
• Generous time-off policy and paid holidays.
• Opportunities for professional development and continuous learning.
• Retirement plan eligibility and employer contributions.
• Supportive, diverse, and inclusive work culture with global collaboration.

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!

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