Senior Machine Learning Engineer (Remote) at Jobgether

, New Jersey, United States -

Full Time

Start Date

Immediate

Expiry Date

18 Feb, 26

Salary

0.0

Posted On

20 Nov, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine Learning, MLOps, Pipeline Optimization, Production Deployment, Cloud Platforms, Containerization, Orchestration Tools, ML Frameworks, Problem-Solving, Collaboration, Communication, Innovation, Efficiency, Continuous Improvement

Industry

Internet Marketplace Platforms

Description

This position is posted by Jobgether on behalf of a partner company. We are currently looking for a Senior Machine Learning Engineer in New Jersey (USA). As a Senior Machine Learning Engineer, you will play a critical role in building and optimizing scalable ML pipelines, ensuring seamless deployment and high performance of models in production environments. You will collaborate closely with software engineers, data scientists, and cross-functional teams to design and implement robust data and prediction pipelines, automate workflows, and improve orchestration processes. This position offers the opportunity to influence ML infrastructure, drive operational efficiency, and implement best practices for model management and deployment. You will work in a fast-paced, innovative environment where problem-solving, technical excellence, and proactive collaboration are highly valued. Your work will directly impact the reliability, performance, and scalability of ML solutions while supporting the broader data-driven strategy. Accountabilities: • Design and implement scalable and efficient data pipelines for training and deploying ML models. • Build prediction pipelines and APIs to integrate trained models into production systems. • Develop and manage deployment strategies to ensure security, reliability, and scalability. • Implement monitoring and logging solutions to track model performance, data quality, and system health. • Optimize orchestration processes to improve efficiency and reduce infrastructure costs. • Collaborate with cross-functional teams to identify bottlenecks and implement workflow improvements. • Maintain best practices for ML lifecycle management and operational excellence. • Bachelor’s degree in Computer Science, Engineering, Mathematics, or a related technical field. • 5–7 years of experience in MLOps, ML engineering, or a similar role, with proven expertise in pipeline optimization and production deployment. • Proficiency with cloud platforms (AWS, Azure, GCP) and containerization technologies (Docker, Kubernetes). • Experience with orchestration tools such as Airflow, Kubeflow, or MLflow. • Strong knowledge of ML frameworks such as TensorFlow, PyTorch, or Scikit-Learn. • Experience deploying ML models in production and managing the model lifecycle. • Excellent problem-solving skills, with the ability to work independently and collaboratively. • Strong communication skills for cross-functional collaboration. • Entrepreneurial mindset with a focus on innovation, efficiency, and continuous improvement. • Competitive salary and compensation package. • Generous health, dental, and vision benefits. • Flexible work arrangements with potential remote-friendly options. • Paid time off and parental leave. • Retirement benefits, including 401(k) matching. • Professional development support and opportunities for growth. • Inclusive and diverse workplace culture that encourages collaboration and innovation. 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! #LI-CL1

Responsibilities

Design and implement scalable data pipelines for ML models and build prediction pipelines to integrate models into production systems. Collaborate with cross-functional teams to improve workflows and maintain best practices for ML lifecycle management.