Machine Learning Engineer at Jobgether – United States

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

This role offers an exciting opportunity to bridge cutting-edge AI research with production-grade systems in a fast-paced, high-impact environment. You will lead complex applied research projects, designing and optimizing deep learning architectures that power critical decision-making systems. The position emphasizes both technical autonomy and strategic impact, requiring collaboration across data, infrastructure, and product teams. You will ensure models are scalable, efficient, and aligned with business goals, while mentoring peers and contributing to technical standards. This is ideal for engineers passionate about applied AI, foundation models, and production-ready machine learning systems, who thrive on solving ambiguous problems and delivering measurable outcomes. The role provides exposure to state-of-the-art architectures, cross-functional teams, and opportunities to influence AI strategy at scale.

• Lead and execute applied research initiatives in deep learning and foundation models for critical business systems.

• Design architectures that bridge research and production, optimized for latency, interpretability, and cost-efficiency.

• Collaborate with cross-functional teams to integrate research outputs into production models, ensuring measurable business impact.

• Establish technical standards for experimentation, model evaluation, and code quality within the AI team.

• Mentor senior engineers and researchers, providing guidance on deep learning, problem formulation, and research methodology.

• Contribute to internal thought leadership, papers, and research initiatives aligned with strategic AI objectives.

Requirements:

• 5–7+ years of applied AI/ML experience with a proven record of delivering research-driven systems into production.

• Deep expertise in deep learning architectures (Transformers, multimodal models, or GNNs).

• Strong programming skills in Python and proficiency with frameworks such as PyTorch, JAX, or TensorFlow.

• Solid understanding of MLOps principles and the constraints of deploying models at scale.

• Advanced skills in ML problem formulation, experimentation, and navigating ambiguous or incomplete data.

• Ability to communicate complex technical concepts effectively to both technical and non-technical stakeholders.

• Experience with large-scale experimentation, A/B testing, and performance evaluation.

• Demonstrated capacity to mentor peers and contribute to team growth.

Benefits:

• Opportunity to earn equity in the company.

• Medical, dental, and vision insurance.

• Life insurance and AD&D coverage.

• Extended maternity and paternity leave.

• Access to internal learning platforms and language programs.

• Mental health and wellness assistance programs.

• 401(k) and flexible saving plans (HSA/FSA).

• Work-from-home allowance and relocation assistance, if applicable.

• Exposure to cutting-edge AI technologies and impactful projects at scale.