Whatnot - Senior Engineering Manager, ML Platform
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Requirements
• Intellectually curious, deeply technical leaders eager to shape the future of AI and ML at Whatnot; strong technical depth required for a management role in infrastructure development. • Experience leading teams that build systems powering machine learning models across critical business surfaces such as growth, recommendations, trust and safety, fraud detection, seller tooling, etc. (years of experience not explicitly stated). • Familiarity with building low-latency deep learning model serving solutions; expertise in streaming feature ingestion systems for real-time data processing is a must. • Experience guiding the prototyping and productionization of novel ML architectures that shape user experiences directly (years of experience not explicitly stated). • Ability to design, scale inference infrastructure capable of serving large models with low latency and high throughput; knowledge in GPU utilization for distributed training is required. • Experience overseeing real-time feature pipelines ensuring single-second feedback from behavioral signals while maintaining reliability and model fidelity (years of experience not explicitly stated). • Skill set to drive improvements on the scienti
Responsibilities
• 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. • US Based: We offer flexibility to work from home or from one of our global office hubs, and we value in-person time for planning, problem-solving, and connection. Team members in this role must live within commuting distance of our New York, Seattle, Los Angeles, and San Francisco hubs. • Curious about who thrives at Whatnot? We’ve found that low ego, a growth mindset, and leaning into action and high impact goes a long way here. • As our next Sr. 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 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.
Benefits
• For US-based applicants: $255,000 - $345,000/year + benefits + stock options • The salary range may be inclusive of several levels that would be applicable to the position. Final salary will be based on a number of factors including, level, relevant prior experience, skills and expertise. This range is only inclusive of base salary, not benefits (more details below) or equity in the form of stock options. • Generous Holiday and Time off Policy • Health Insurance options including Medical, Dental, Vision • Work From Home Support • Monthly allowance for cell phone and internet • Monthly allowance for wellness • Annual allowance towards Childcare • Lifetime benefit for family planning, such as adoption or fertility expenses • Retirement; 401k offering for Traditional and Roth accounts in the US (employer match up to 4% of base salary) and Pension plans internationally • Monthly allowance to dogfood the app • All Whatnauts are expected to develop a deep understanding of our product. We're passionate about building the best user experience, and all employees are expected to use Whatnot as both a buyer and a seller as part of their job (our dogfooding budget makes this fun and easy!). • 16 weeks of paid parental leave + one month gradual return to work *company leave allowances run concurrently with country leave requirements which take precedence.
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