Senior Machine Learning Engineer, Recommendations (Experience) in United States Embassy at Jobgether

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior Machine Learning Engineer, Recommendations (Experience) based in United States.

We are seeking a Senior Machine Learning Engineer to build intelligent, ML-powered experiences that improve personalization and user engagement at scale.
This role focuses on developing recommendation systems that connect millions of users with relevant content through advanced machine learning technologies.
You will work across the entire ML lifecycle, from data pipelines and feature engineering to model deployment and real-time serving.
The position combines strong software engineering practices with machine learning expertise to create reliable, impactful products.
You will collaborate closely with product, design, engineering, and platform teams to transform user needs into innovative solutions.
This is an opportunity to influence the future of personalized experiences while working with large-scale data, AI systems, and modern cloud technologies.

The Senior Machine Learning Engineer will be responsible for designing, building, and scaling machine learning systems that deliver personalized experiences to users. The role requires ownership of end-to-end ML solutions, strong engineering execution, and collaboration across multiple teams. Key responsibilities include:

• Develop, test, deploy, and maintain machine learning and LLM-based systems serving real-world users.
• Design and implement complete ML pipelines covering data processing, feature engineering, model training, deployment, and monitoring.
• Build scalable solutions capable of processing large volumes of user data and supporting high-performance recommendation experiences.
• Make technical decisions by balancing system performance, cost efficiency, scalability, complexity, and long-term maintainability.
• Work with distributed technologies and data platforms to create reliable ML infrastructure and production systems.
• Establish monitoring, experimentation frameworks, A/B testing processes, and metrics to measure user impact.
• Troubleshoot complex challenges across data pipelines, machine learning models, APIs, and distributed systems.
• Contribute to technical strategy, engineering best practices, and continuous improvement initiatives.
• Leverage AI-assisted engineering approaches and agentic workflows to improve productivity and accelerate delivery.
• Collaborate with Product, Design, Engineering, and Platform teams to build user-focused solutions.

The ideal candidate is an experienced machine learning engineer with strong software engineering foundations and a passion for building production-grade AI systems. Required qualifications include:

• 4+ years of software engineering experience with proven ability to build scalable production systems.
• 1-2+ years of hands-on experience developing and operating machine learning systems in production environments.
• Strong programming skills in Python and Scala or Java/JVM, with experience writing maintainable, production-quality code.
• Experience building and deploying ML models end-to-end, including data preparation, training, serving, and monitoring.
• Experience developing and integrating LLM-powered features into production applications.
• Familiarity with retrieval-augmented generation, model serving, and modern AI system architectures.
• Strong understanding of recommendation systems, search systems, or shared ML architectures across domains.
• Advanced SQL skills and experience working with large-scale datasets using technologies such as BigQuery or Spark.
• Experience with cloud platforms such as AWS or GCP and containerization technologies including Docker and Kubernetes.
• Experience designing distributed data processing workflows and ETL pipelines using tools such as Airflow or Spark.
• Familiarity with machine learning frameworks such as TensorFlow or PyTorch.
• Strong focus on data quality, reliability, and understanding how upstream data impacts user experiences.
• Excellent problem-solving, communication, and collaboration skills in cross-functional environments.

The company offers a flexible and inclusive environment designed to support employee growth, creativity, and wellbeing. Benefits include:

• Flexible working culture with opportunities to work remotely or collaborate from office locations.
• Relocation support, including allowances, travel assistance, temporary accommodation, and local onboarding support where applicable.
• Employee equity plan.
• Generous professional development allowance to support continuous learning and career growth.
• Flexible vacation policy with up to 35 days of paid time off annually.
• Creativity and wellness benefits supporting personal interests such as fitness, courses, and learning activities.
• Free language learning opportunities.
• Office perks including snacks, benefits, and complimentary meals when working onsite.
• A strong commitment to diversity, equity, inclusion, and creating a workplace where everyone can contribute and thrive.