AdTechTalent
Engineering114 days agoOn-site

Moloco

Machine Learning Engineer, Infrastructure

machine learningml infrastructurepythonjavac++rusttensorflowpytorchkerasjaxawsgcpazuredockerkubernetesapache beamapache sparkairflowgputpumlopsdata pipelinesci/cd

Key details

Salary

$167K – $210K

Employment type

Full-time

Seniority

Mid-level

Years experience

3-5

Location

Menlo Park, California, United States

Full job description

Moloco seeks a Machine Learning Engineer focused on ML infrastructure to develop and optimize scalable machine learning systems supporting large-scale ad serving and model training. Responsibilities include designing and maintaining ML infrastructure, developing data pipelines, collaborating with cross-functional teams, implementing CI/CD for ML, operating high-performance ML systems using JAX and Rust on GPUs and TPUs, monitoring system performance, integrating new tools, automating workflows with AI agents, conducting experiments to improve ad quality, and documenting best practices. Candidates should have 4+ years of experience in ML engineering or related fields, proficiency in Python, Java, C++, or Rust, experience with ML frameworks (TensorFlow, PyTorch, Keras, JAX), cloud platforms (AWS, GCP, Azure), containerization tools (Docker, Kubernetes), and scalable data pipeline tools (Apache Beam, Spark, Airflow). Strong problem-solving skills, collaboration, and ability to work in ambiguous environments are required. The role is full-time, located in Menlo Park, California, with a salary range of $167,200 to $210,000 USD and a competitive benefits package.

What you'll do

  • Design, build, and maintain robust machine learning infrastructure to support large-scale ad serving and model training at a global scale
  • Develop and optimize data pipelines and workflows for efficient model deployment and monitoring
  • Collaborate with cross-functional teams including data scientists, product managers, and software engineers to deliver end-to-end ML solutions
  • Implement best practices for model versioning, reproducibility, and continuous integration/continuous deployment (CI/CD) in ML systems
  • Build and operate high performance ML systems using modern frameworks and languages such as JAX and Rust, optimized for execution on GPUs and TPUs
  • Monitor, troubleshoot, and continuously improve the reliability, scalability, and performance of ML systems delivering millions of predictions per second worldwide
  • Evaluate and integrate new tools, frameworks, and technologies to enhance the ML platform’s capabilities
  • Integrate AI-driven agents into the core engineering and modeling lifecycle to automate and amplify the team's impact
  • Contribute to the design and execution of experiments to improve ad quality and system performance
  • Document system architecture, processes, and best practices to ensure knowledge sharing and maintainability

Requirements

  • 4+ years of experience in machine learning engineering, ML infrastructure, or a related field
  • Proficiency in programming languages such as Python, Java, C++, or Rust
  • Hands-on experience with ML frameworks and libraries (e.g., TensorFlow, PyTorch, Keras, Jax)
  • Experience with cloud platforms (e.g., AWS, GCP, Azure) and containerization/orchestration tools (e.g., Docker, Kubernetes)
  • Experience building and maintaining scalable data pipelines using tools like Apache Beam, Apache Spark, Airflow, or similar
  • Ability to thrive in ambiguous environments and proactively solve complex infrastructure challenges
  • Effective collaboration with cross-functional teams
  • Strong problem-solving skills and a growth mindset

Tech stack

PythonJavaC++RustTensorFlowPyTorchKerasJAXAWSGCPAzureDockerKubernetesApache BeamApache SparkAirflowGPUTPU

Benefits

Competitive benefits packageInnovative benefits that empower employees to take care of themselves and their familiesInclusive work environmentOpportunities for growth and learning

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