London - Senior ML Ops Engineer (Experiences)
About this role
Engineers in this group build and support the marketplace for travel experiences by developing robust infrastructure and managing machine learning pipelines. The team focuses on transforming how artificial intelligence and data science drive innovation, specifically handling increasing traffic and supporting pre-computed, batch, and real-time model needs.
Candidates joining the Internal Systems pillar will provide tools and support infrastructure for seamless execution of machine learning processes while collaborating with cross-functional stakeholders. The position suits individuals who take ownership of end-to-end software engineering tasks, from design and implementation to quality assurance and maintenance.
Tripadvisor operates this hybrid position from London, welcoming applicants already based in the city or living a maximum of one and a half hours away with a requirement to work on-site twice per month.
What you will do
- Empower data science and machine learning stakeholders by providing necessary infrastructure and tools for seamless process execution.
- Support technology stack evolution by developing across existing AWS systems and adopting new environment solutions.
- Build and maintain scalable infrastructure supporting pre-computed, batch, and real-time models to reduce time-to-market.
- Manage all aspects of software engineering, ranging from design and implementation to quality assurance and maintenance.
- Monitor and support machine learning pipelines for accuracy, drift, service-level agreements, billing volumes, and enhancements.
- Collaborate with cross-functional stakeholders to define and document requirements for impactful artificial intelligence products.
What you need
- Bring over four years of MLOps experience building modern machine learning infrastructure for all lifecycle stages.
- Demonstrate hands-on proficiency with AWS or GCP cloud services for scalable and reliable solutions.
- Show demonstrable production experience utilizing infrastructure-as-code tools such as Terraform or CloudFormation.
- Develop across a diverse technology stack with a willingness to learn and adopt new technologies.
- Participate effectively in cross-functional teams within fast-paced environments while maintaining high-quality communication skills.
- Possess experience with continuous integration and continuous deployment processes and platforms.
Nice to have
- Previous exposure to Seldon, KServe, RayServe, Kubernetes, MLFlow, Sagemaker, Kubeflow, Anyscale, Valohai, ArgoCD, Docker, Python, Java, Ray, Spark, Pandas, Argo Workflow, Postgres, Snowflake, and BigQuery
- Previous knowledge managing complementary databases such as vector or graph databases
- Hands-on experience in model optimization for high throughput and low latency applications with cost awareness
- Experience with LLMOps leveraging open source models
Benefits
- Competitive compensation packages including base salary, annual bonus, and equity
- Flexible schedule and work-your-way remote-friendly collaboration approach
- Donation matching for qualifying charitable contributions annually
- Tuition assistance for qualified learning programs
- Lifestyle benefit for personal travel or wellness
- Health benefits with great coverage and competitive premiums
Skills and tech stack
Questions candidates ask
What is the remote policy for this role?
This is a hybrid role based in London, opened for all the candidates who are already based in London or max 1,5h away. It features a relaxed hybrid schedule requiring on-site presence twice per month.
What compensation and perks are included?
The position offers competitive compensation packages including base salary, annual bonus, and equity. Additional perks include tuition assistance, lifestyle benefits, travel discounts, donation matching, and health coverage.
What does the tech stack look like?
The environment runs primarily on AWS cloud infrastructure utilizing infrastructure-as-code tools like Terraform or CloudFormation. Engineers also work with technologies such as Kubernetes, Docker, Python, Java, Spark, and various deployment and orchestration frameworks.