Senior Staff Machine Learning Engineer (Remote - US)

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

This role offers an exciting opportunity to lead the design and deployment of cutting-edge machine learning systems that directly impact product performance and business growth. You will be responsible for developing scalable, production-ready ML models, building robust data pipelines, and optimizing algorithms for real-world use. Collaborating with cross-functional teams, you will help translate complex business challenges into innovative ML solutions. The position emphasizes end-to-end ownership of machine learning projects, continuous model monitoring, and staying ahead of industry advancements. You will work in a collaborative, inclusive, and fast-paced environment that encourages technical excellence and experimentation.

Accountabilities

• Own the full lifecycle of machine learning projects, from data collection and preprocessing to model deployment, monitoring, and maintenance.
• Build and maintain robust, scalable data pipelines that support model development, training, and production deployment.
• Implement ML algorithms and models that meet performance, scalability, and reliability goals in production environments.
• Collaborate with data scientists, engineers, and product teams to design and deliver ML systems addressing business and product needs.
• Continuously monitor and improve model performance, including hyperparameter tuning, experimentation, and validation.
• Leverage distributed computing frameworks and cloud platforms for efficient large-scale data processing.
• Stay current on advances in ML, software engineering practices, and deployment strategies to maintain state-of-the-art solutions.

• Master’s or Ph.D. in Computer Science, Engineering, or a related field.
• 6+ years of experience as a Machine Learning Engineer, with expertise in production-grade model development and deployment.
• Strong proficiency in Python or similar programming languages, with experience using ML libraries like TensorFlow, PyTorch, or scikit-learn.
• Experience with cloud platforms (AWS, GCP, Azure) and distributed computing frameworks (Spark, Kubernetes).
• Proven ability to implement end-to-end ML pipelines, including data preprocessing, model deployment, and monitoring.
• Strong background in model optimization, version control, and CI/CD practices for machine learning.
• Excellent problem-solving skills and the ability to collaborate across multidisciplinary teams.

• Competitive salary: NY/NJ/CA: $209,000–$225,000; other U.S. states: $188,000–$203,000.
• 100% company-paid medical, dental, and vision coverage.
• 401(k) plan with company equity participation.
• Unlimited paid time off and 13 company-paid holidays.
• Paid parental leave and wellness support programs.
• Flexible remote work environment.
• Free subscription to company services and other employee perks.

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