Senior Engineering Manager, ML Platform

About Whatnot

Whatnot is the largest live shopping platform in North America and Europe to buy, sell, and discover the things you love. We’re re-defining e-commerce by blending community, shopping, and entertainment into a community just for you. As a remote co-located team, we’re inspired by innovation and anchored in our values. With hubs in the US, UK, Germany, Ireland, Poland, and Australia, we’re building the future of online marketplaces –together.

From fashion, beauty, and electronics to collectibles like trading cards, comic books, and even live plants, our live auctions have something for everyone. And we’re just getting started! As one of the fastest growing marketplaces, we’re looking for bold, forward-thinking problem solvers across all functional areas. Check out the latest Whatnot updates on our news and engineering blogs and join us as we enable anyone to turn their passion into a business, and bring people together through commerce.

The Role: Senior Engineering Manager, ML Platform

We’re looking for hands-on builders–intellectually curious, deeply technical leaders eager to shape the future of AI and ML at Whatnot. You’ll lead the development and scaling of the core infrastructure that powers machine learning and self-hosted large language model applications across the company, working side by side with machine learning scientists to bring cutting-edge models powered by near-realtime features into production and unlock entirely new product experiences. This means building systems that make advanced ML dependable and fast at scale–from low-latency deep learning model serving and streaming feature ingestion to distributed training and high-throughput GPU inference. This is a management role that requires strong technical depth–potential candidates should be excited about getting and staying in the weeds. You will be expected to up-level architectural discussion, provide technical feedback, and code at least a day a week.

What You'll Do

• Own the infrastructure powering AI and ML models across critical business surfaces–supporting growth, recommendations, trust and safety, fraud, seller tooling, and more.
• Guide the prototyping, deployment, and productionization of novel ML architectures that directly shape user experience and marketplace dynamics.
• Help design and scale inference infrastructure capable of serving large models with low latency and high throughput.
• Oversee and evolve real-time feature pipelines that feed both our online and offline stores, ensuring single-second feedback from behavioral signals, high reliability, and model training fidelity.
• Drive feature platform improvements and expand scope to cover non-ML use cases such as fraud rules where point-in-time backtesting is also critical.
• Lead the development of distributed training and inference pipelines leveraging GPUs and both model and data parallelism.
• Optimize system performance by managing resource utilization and developing intelligent feature caching strategies.
• Empower scientists to iterate faster by building abstractions, APIs, and developer tools that simplify the development of near-realtime features and model iteration.
• Roll out ever-better ergonomics around model training and deployment.
• Stretch beyond your comfort zone to take on new technical challenges as we scale AI across Whatnot’s ecosystem.

You Bring

As our next Senior Engineering Manager, ML Platform, you should have 4+ years of engineering management experience developing production machine learning systems at consumer-scale loads, plus:

• Bachelor’s degree in Computer Science, Statistics, Applied Mathematics or a related technical field, or equivalent work experience.
• 5+ years of hands-on software engineering experience building and maintaining production systems for consumer-scale loads.
• 1+ years of professional experience developing software in Python.
• Ability to work autonomously and drive initiatives across multiple product areas and communicate findings with leadership and product teams.
• Experience with operational, search, and key-value databases such as PostgreSQL, DynamoDB, Elasticsearch, Redis.
• Experience working with ML-specific tools and frameworks such as MLFlow, LitServe, TorchServe, Triton.
• Firm grasp of visualization tools for monitoring and logging e.g. DataDog, Grafana.
• Familiarity with cloud computing platforms and managed services such as AWS Sagemaker, Lambda, Kinesis, S3, EC2, EKS/ECS, Apache Kafka, Flink.
• Professionalism around collaborating in a remote working environment and well tested, reproducible work.
• Exceptional documentation and communication skills.

Compensation & Benefits

For US-based applicants, the compensation range is $255,000 - $345,000/year, plus benefits and stock options. The final salary will be based on factors including level, relevant prior experience, skills, and expertise. This range is inclusive of base salary only.

Benefits include: Generous Holiday and Time off Policy, Health Insurance options (Medical, Dental, Vision), Work From Home Support (home office setup allowance, monthly allowance for cell phone and internet), Care benefits (monthly allowance for wellness, annual allowance for childcare, lifetime benefit for family planning), Retirement (401k with employer match in US, Pension plans internationally), Monthly allowance to dogfood the app, and 16 weeks of paid parental leave + one month gradual return to work.

Key skills/competency

• Machine Learning Infrastructure
• Distributed Systems
• AI/ML Platform
• Deep Learning Serving
• Real-time Feature Pipelines
• GPU Inference
• Python Development
• AWS Cloud Services
• Engineering Leadership
• System Scalability