Senior Machine Learning Engineer

What we do:

Zefr is the leading global technology company enabling responsible marketing in walled garden social environments. Zefr’s solutions empower brands to manage their content adjacency on scaled platforms such as YouTube, Meta, TikTok, and Snap, in accordance with industry standard frameworks. Through its patented AI technology, Zefr offers brands and agencies more accurate and transparent solutions for social walled gardens. The company is headquartered in Los Angeles, California, with additional locations across the globe.

What you’ll do:

We are hiring a Senior Machine Learning Engineer focused in ML Operations to be involved in designing and building large-scale applications and systems to acquire, process, and store multi-terabytes of YouTube, TikTok, Facebook and other social media data. In this role you will implement infrastructure to support machine learning systems at scale processing hundreds of millions of social media videos daily. You will be working with state of the art models, including large language models, to build sophisticated compound AI systems. We are excited to welcome someone who is passionate about cutting edge research in machine learning and computer vision. We want an individual who can keep up to date with the ever-expanding field of Machine Learning Operations.

This is a role where we both expect to learn from you and have you learn from us!

Tech stack:

• Languages: Python, SQL

• Data Stores: Snowflake, Qdrant, GCS

• Data Processing: DBT, Pandas, Ray

• DevOps: GitHub Actions, Docker, Terraform, Kubernetes, ArgoCD, AWS, GCP, Datadog

• MLOps: Triton Inference Server, Weights and Biases, ONNX, TensorRT LLM, vLLM, SGLang

• ML: Voxel51 Teams, Transformers, PyTorch, HuggingFace

What we’re looking for:

• Bachelor's or Master's degree in Computer Science or related field with 5+ years of professional experience in ML Engineering or MLOps

• Strong foundation in mathematics and statistics

• Deep expertise in ML model deployment, serving infrastructure, and production ML systems

• Hands-on experience with transformer architectures (e.g., BERT, ViT) for natural language and vision tasks

• Strong understanding of multimodal embedding techniques for integrating text, image, audio, and structured data

• Experience with LLM models such as Gemini, GPT, Claude, Qwen, etc.

• Experience with ML experiment tracking, model versioning, and feature stores

• Strong understanding of CI/CD principles applied to ML workflows

• Experience optimizing model inference performance (ONNX, TensorRT, or similar)

• Openness to new technologies and creative solutions

Nice to Have

• Experience with ad tech and digital advertising ecosystem

• Experience with multimodal LLM fine-tuning

Benefits (for US based employees):

• Flexible PTO

• Medical, dental, and vision insurance with FSA options

• Company-paid life insurance

• Paid parental leave

• 401(k) with company match

• Professional development opportunities

• 14 paid holidays off

• Flexible hybrid work schedule

• “Summer Fridays” (shorter work days on select Fridays during the summertime)

• In-office lunches and lots of free food

• Optional in-person and virtual events (we like to celebrate!)

Compensation (for US based employees):

The anticipated base salary for this position is between $150,000 and $200,000. Within the range, individual pay is determined by factors such as job-related skills, experience, and relevant education or training. If your compensation expectations fall outside of this range, it may still be worth having a conversation.

Zefr is an equal opportunity employer that embraces diversity and inclusion in the workplace. We are committed to building a team that represents a variety of backgrounds, skills, and perspectives because we know this only makes us better. We strongly encourage women, persons of color, LGBTQIA+ individuals, persons with disabilities, members of ethnic minorities, foreign-born residents, and veterans to apply even if you do not meet 100% of the qualifications.