Staff Machine Learning Engineer, Home Surfaces

About This Opportunity

We are representing a confidential streaming technology company seeking a Staff Machine Learning Engineer to join their personalization engineering team. This role focuses on building intelligent, moment-based experiences that help millions of users discover and engage with content that matters most to them.

As a senior member of the machine learning team, you'll architect and deploy recommendation systems at global scale, working at the intersection of large language models, ranking systems, and production ML infrastructure. You'll drive technical direction in ambiguous problem spaces and shape the future of personalized discovery experiences.

Responsibilities

• Own and improve machine learning models and systems powering personalized home feed experiences
• Design, build, and ship recommendation systems serving millions of users globally
• Develop content recommendation capabilities for emerging AI-powered user experiences
• Train, fine-tune, evaluate, and optimize large language models using supervised fine-tuning (SFT), distillation, and parameter-efficient training approaches
• Partner with product managers, engineers, data scientists, and designers to define experimentation strategies
• Drive A/B testing, monitoring, model evaluation, and continuous optimization of recommendation quality, reliability, and cost efficiency
• Improve ML platform capabilities, data pipelines, and production systems supporting personalization at scale
• Drive technical direction and contribute to long-term architecture of personalization systems
• Mentor machine learning engineers and raise technical standards across the team

What We're Looking For

• 8+ years of experience building and deploying machine learning systems in production environments
• Deep expertise in recommendation systems, ranking models, personalization, or large-scale content discovery platforms
• Strong proficiency in Python and hands-on experience building ML systems with PyTorch
• Experience with large language model training, fine-tuning, evaluation, and optimization techniques including SFT, distillation, and LoRA
• Track record working with large-scale inference systems and addressing latency, reliability, and cost optimization challenges
• Commitment to creating high-quality user experiences through thoughtful application of machine learning
• Ability to communicate effectively across technical and non-technical audiences and influence decisions beyond your immediate team
• Experience designing, executing, and interpreting online experiments and A/B tests
• Experience operating distributed machine learning workloads using technologies such as Ray, FSDP, HSDP, or similar frameworks
• Experience building and maintaining data pipelines and orchestration workflows using Flyte, Airflow, BigQuery, and cloud-based storage platforms

Location: United States (remote within North Americas region). Team operates within Eastern Standard time zone for collaboration.

How We Work With You

Our talent team at Get A Job.ai partners with leading technology companies to connect exceptional candidates with transformative opportunities. When you apply through our platform, a dedicated recruiter will review your background and conduct an initial screening. If there's a strong match, we'll submit your profile to our client and guide you through their interview process. Please do not contact the client directly—all communication and coordination will flow through Get A Job.ai to ensure the best candidate experience.

Pay

Pay via Get A Job.ai will be competitive and commensurate with experience. Our client offers comprehensive benefits including health insurance, six months paid parental leave, 401(k) retirement plan, monthly meal allowance, 23 paid days off, 13 paid flexible holidays, and paid sick leave.

Get A Job.ai is an equal opportunity recruiter. We welcome and encourage applications from candidates of all backgrounds, experiences, and perspectives. We are committed to fostering an inclusive recruitment process and workplace.