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Machine Learning Infrastructure Jobs in California

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Machine Learning Infrastructure information

What is the difference between Machine Learning Infrastructure vs Data Engineer?

AspectMachine Learning InfrastructureData Engineer
Required CredentialsBachelor's in CS, experience with ML toolsBachelor's in CS, experience with data pipelines
Work EnvironmentFocus on ML systems, cloud platformsData pipelines, database management
Employer & Industry UsageTech companies, AI startupsAny industry with data needs, tech firms
Search & Comparison IntentUnderstanding ML system setupBuilding data pipelines

Machine Learning Infrastructure specialists focus on deploying and maintaining systems that support machine learning models, often working with cloud platforms and ML tools. Data Engineers build and manage data pipelines and databases, supporting data collection and processing. While both roles require technical skills and overlap in data handling, Machine Learning Infrastructure is more centered on ML system deployment, whereas Data Engineers focus on data architecture and pipelines.

What are the typical challenges faced by professionals working in Machine Learning Infrastructure roles?

Professionals in Machine Learning Infrastructure often encounter challenges related to scaling systems to handle large datasets, ensuring model reproducibility, and maintaining efficient workflows for both development and deployment. Collaborating closely with data scientists, software engineers, and DevOps teams is crucial to address issues like version control, resource allocation, and performance optimization. Staying updated on evolving tools and cloud platforms is also essential, as the landscape changes rapidly and impacts system design and integration.

What are the key skills and qualifications needed to thrive in Machine Learning Infrastructure, and why are they important?

To excel in Machine Learning Infrastructure, you need a solid background in computer science, software engineering, and distributed systems, often supported by experience in deploying and scaling machine learning models. Familiarity with cloud platforms (like AWS, GCP, or Azure), containerization tools (such as Docker and Kubernetes), and ML workflow systems (e.g., TensorFlow Extended, MLflow) is crucial. Strong problem-solving skills, collaboration, and the ability to communicate technical concepts effectively help you stand out in this field. These skills ensure scalable, reliable, and efficient deployment of ML solutions, enabling organizations to leverage machine learning at production scale.

What is machine learning infrastructure?

Machine learning infrastructure refers to the combination of hardware, software, platforms, and tools necessary to support the development, training, deployment, and maintenance of machine learning models at scale. This includes computing resources like GPUs and CPUs, data storage systems, workflow orchestration tools, model serving frameworks, and monitoring solutions. The goal of ML infrastructure is to streamline and automate the machine learning lifecycle, enabling data scientists and engineers to build and deploy models more efficiently and reliably.
What are popular job titles related to Machine Learning Infrastructure jobs in California? For Machine Learning Infrastructure jobs in California, the most frequently searched job titles are:
What job categories do people searching Machine Learning Infrastructure jobs in California look for? The top searched job categories for Machine Learning Infrastructure jobs in California are:
Machine Learning Infrastructure jobs near you
Software Engineer, Machine Learning Infrastructure

Software Engineer, Machine Learning Infrastructure

Whatnot

San Francisco, CA • On-site

$190K - $300K/yr

Full-time

Medical, Dental, Vision, Retirement

Posted 22 days ago

Job description

Join the Future of Commerce with Whatnot!
Whatnot is the largest livestream shopping platform in North America and Europe to buy, sell, and discover the things you love. Whether it's trading cards, fashion, electronics, or live plants, our sellers are building real businesses across hundreds of categories. We're building live commerce at a scale that's never been done in the West, and there's no playbook to copy. The people here are shaping how an entirely new industry develops.
As a remote co-located team, we're inspired by our values and anchored in hubs across the US, UK, Ireland, Poland, Germany, and Australia. We move fast, stay close to our users, and focus on the work that drives the most impact.
We're one of the fastest growing marketplaces and were recently named the #1 Best Startup Employer in America by Forbes. 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.
Role
We're looking for builders-intellectually curious, highly entrepreneurial engineers eager to shape the future of AI and ML at Whatnot. You'll design and scale 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 into production and unlock entirely new product experiences. This means building systems that make advanced ML dependable and fast at scale-from low-latency, large model serving to distributed training & high-throughput GPU inference.
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.
  • Prototype, deploy, and productionalize novel ML architectures that directly shape user experience and marketplace dynamics.
  • Design and scale inference infrastructure capable of serving large models with low latency and high throughput.
  • Build distributed training and inference pipelines leveraging GPUs and both model and data parallelism.
  • 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.
You
People who do well at Whatnot tend to be comfortable figuring things out as they go, biased toward action, and genuinely curious about what they're building. They care more about outcomes than credit and stay close to the product and the people using it.
As our next AI/ML Platform Engineer you should have 4+ years of professional experience developing machine learning systems and algorithms, plus:
  • Bachelor's degree in Computer Science, Statistics, Applied Mathematics or a related technical field, or equivalent work experience.
  • 3+ years of 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.
  • 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
  • Flexible Time off Policy and Company-wide Holidays (including a spring and winter break)
  • Health Insurance options including 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 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!).
  • Parental Leave
    • 16 weeks of paid parental leave + one month gradual return to work *company leave allowances run concurrently with country leave requirements which take precedence.
EOE
Whatnot is proud to be an Equal Opportunity Employer. We value diversity, and we do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, parental status, disability status, or any other status protected by local law. We believe that our work is better and our company culture is improved when we encourage, support, and respect the different skills and experiences represented within our workforce.

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