Staff Software Engineer, Machine Learning

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

This role offers an opportunity to design, develop, and scale advanced machine learning solutions that impact millions of users. You will work on recommender systems, personalization engines, and engagement-focused ML products, contributing to end-to-end model development from ideation to production. The position emphasizes cross-functional collaboration, enabling you to influence product roadmaps while driving innovation in high-throughput, low-latency systems. You will mentor peers, set technical direction, and help shape the future of ML infrastructure and applications. Ideal candidates thrive in ambiguous environments, enjoy tackling technically challenging problems, and are motivated by creating real-world user impact at scale.

Accountabilities

• Architect and build robust, scalable, and high-throughput recommender systems that power core product features.
• Design, train, and deploy advanced machine learning models using deep learning, reinforcement learning, and optimization techniques.
• Lead technical initiatives, setting architectural direction, and addressing systemic challenges.
• Collaborate cross-functionally with product, design, and engineering teams to shape ML-driven product roadmaps.
• Identify opportunities for innovation and efficiency in ML system design and deployment.
• Mentor and guide team members, promoting engineering excellence and best practices.

• 8+ years of experience in applied Machine Learning, including Ph.D. or Master’s degree in Computer Science, Machine Learning, AI, or related field.
• Deep expertise in recommender system architectures (e.g., two-tower, transformer-based, multi-task, wide-and-deep models).
• Proficiency in Python and ML frameworks such as PyTorch, JAX, or TensorFlow.
• Extensive experience designing and deploying large-scale ML systems in production.
• Strong product intuition and ability to build ML solutions grounded in user feedback and measurable impact.
• Excellent communication and collaboration skills for leading cross-functional technical initiatives.
• Comfort with ambiguity and ability to tackle open-ended, technically challenging problems.

Bonus Points:

• Experience with multi-objective ranking or whole-page optimization.
• Knowledge of notification systems or reinforcement learning in production.
• Expertise in user representation learning or personalization.
• Experience with distributed training (e.g., PyTorch DDP, Ray Train) and large-scale data pipelines (e.g., Spark, Flink).

• Competitive base salary with equity participation and bonus potential.
• Flexible, remote-first work environment with optional office presence.
• Comprehensive health, dental, and vision insurance.
• Opportunities for professional development and career growth.
• Work on challenging, high-impact projects with cutting-edge ML technologies.
• Supportive and inclusive culture emphasizing collaboration and mentorship.

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 for 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, it is shared directly with the hiring company, whose internal team will make the final decision and contact selected candidates for the next steps.

Thank you for your interest!

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