Software Engineer Leadership, Machine Learning RecSys

Meta

$130K — $180K *
Information Technology
Less than 5 years of experience
Job Overview by Ladders

Qualifications

  • Bachelor's degree in Computer Science or equivalent experience
  • 4+ years in software engineering or related field (3+ years with PhD)
  • 3+ years in machine learning, recommendation systems, or AI
  • Experience with building scalable machine learning models
  • Proficient in C/C++, Java, and scripting languages like Python
  • Demonstrated experience leading major technical initiatives
  • Hands-on expertise with large language models like BERT or GPT

Responsibilities

  • Collaborate with cross-functional teams to create innovative applications
  • Implement custom user interfaces using cutting-edge programming techniques
  • Develop reusable software components for backend integration
  • Analyze and optimize code for performance and scalability
  • Lead complex technical efforts and mentor peers
  • Architect scalable systems for complex applications
  • Establish ownership of components and understand systems end-to-end

Benefits

  • Flexible working hours
  • Remote work options
  • Professional development opportunities
  • Health and wellness programs
  • Collaborative and innovative work environment
Full Job Description
Responsibilities

Collaborate with cross-functional teams (product, design, operations, infrastructure) to build innovative application experiences Implement custom user interfaces using latest programming techniques and technologies Develop reusable software components for interfacing with back-end platforms Analyze and optimize code for quality, efficiency, and performance Lead complex technical or product efforts and provide technical guidance to peers Architect efficient and scalable systems that drive complex applications Identify and resolve performance and scalability issues Work on a variety of coding languages and technologies Establish ownership of components, features, or systems with expert end-to-end understanding

Minimum Qualifications
• Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
• 4+ years of experience in software engineering, or a relevant field. 3+ years of experience if you have a PhD
• 3+ years of experience in one or more of the following areas: machine learning, recommendation systems, artificial intelligence, or related technical field
• Experience with developing machine learning models at scale from inception to business impact
• Knowledge developing and debugging in C/C++ and Java, or experience with scripting languages such as Python, Perl, PHP, and/or shell scripts
• Experience with scripting languages such as Python, Javascript or Hack
• Experience leading major initiatives successfully, Proven ability to set technical direction and influence product strategy
• Proven experience designing, building, or deploying recommendation systems (e.g., collaborative filtering, content-based, hybrid approaches, personalization at scale)
• Hands-on experience working with large language models (LLMs), such as BERT, GPT, or similar architectures, including fine-tuning, integration, or application in production environments
• Experience building and shipping high quality work and achieving high reliability
• Experienced in utilizing data and analysis to explain technical problems and providing detailed feedback and solutions
• Experience demonstrating technical leadership working with teams, owning projects, defining and setting technical direction for projects

Preferred Qualifications
• Masters degree or PhD in Computer Science or a related technical field
• Exposure to architectural patterns of large scale software applications
• Experience with scripting languages such as Pytorch and TF
• Publications in top-tier conferences/journals, patents, or open-source contributions in the recommendations or LLM space
• Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
• Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
• Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies

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