Fetch helps people live rewarded, with a vision to create the rewards destination for everyone.
The Role
Own the production lifecycle of ranking and recommendation models, including training, validation, deployment, monitoring, retraining, versioning, rollback, and retirement. Build scalable Spark-based feature and data pipelines, orchestration workflows, model hubs, observability, CI/CD, and reliability standards. Partner with data science and engineering teams to improve model quality, deployment speed, infrastructure efficiency, and operational resilience while mentoring teammates.
Summary Generated by Built In
What we’re building and why we’re building it.
Fetch helps people live rewarded every day, with a vision to become the rewards destination for everyone. We turn everyday activities into meaningful rewards, whether it’s grocery shopping, grabbing a quick meal, or playing a favorite mobile game. To date, we’ve awarded more than $1 billion in Fetch Points to our users.
Each day, more than 13 million receipts are submitted on Fetch, providing visibility into over $212 billion in gross merchandise value. This creates the largest retail-agnostic, SKU-level view of household spending, powering Fetch as an outcomes-based advertising platform that helps brands acquire and retain lifelong consumers.
The Fetch app is available on the App Store and Google Play, with more than 6 million five-star reviews from a highly engaged and loyal user base.
It’s not just our users who believe in Fetch: with investments from Softbank, ICONIQ, DST, Greycroft, and partnerships ranging from challenger brands to Fortune 500 companies, Fetch is reshaping how brands and consumers connect in the marketplace. When you work at Fetch, you play a vital role in a platform that drives brand loyalty and creates lifelong consumers with the power of Fetch points. User and partner success are at the heart of everything we do, and we extend that same commitment to our employees.
At Fetch, we value curiosity, adaptability, and the confidence to explore new tools, especially AI, to drive smarter, faster work. You don’t need to be an expert, but you should be ready to learn quickly and think critically. We welcome learners who move fast, challenge the status quo, and shape what’s next, with us. Ranked as one of America’s Best Startup Employers by Forbes for two years in a row, Fetch fosters a people-first culture rooted in trust, accountability, and innovation. We encourage our employees to challenge ideas, think bigger, and always bring the fun to Fetch.
About the RoleFetch is entering its AI-first era, building the intelligent systems that power personalization, relevance, and ranking across our Ads Platform. These systems help Fetch deliver more relevant, adaptive, and high-performing experiences for millions of users while creating measurable value for advertisers.
As a Senior MLOps Engineer, you’ll own the operational foundation that brings ranking and recommendation models from experimentation into reliable production systems. You’ll partner with Data Science, Product, Platform, and Engineering across the full model lifecycle, including reproducible training, validation, deployment, versioning, monitoring, retraining, and retirement.
This is a hands-on technical leadership role operating at the intersection of ML infrastructure, personalization, and large-scale distributed systems. You’ll build reusable pipelines, strengthen observability and real-time learning workflows, establish architectural and reliability standards, and make model health and progress accessible through a shared model hub. Your work will enable Fetch’s Ads Platform to become smarter, faster, and more responsive to changing user behavior over time.
- Own the complete model lifecycle from experimentation through validation, deployment, versioning, retraining, rollback, and retirement.
- Establish clear model promotion criteria, release processes, and rollback paths that enable teams to ship improvements confidently.
- Design and build scalable ML data and feature engineering pipelines, including feature generation, historical aggregations, and training dataset construction, using Spark/PySpark and modern lakehouse technologies such as S3 and Apache Iceberg.
- Develop reliable, incremental data processing pipelines with support for data quality validation, lineage, point-in-time correctness, and training-serving feature consistency.
- Build monitoring for data quality, drift, prediction quality, service reliability, latency, and infrastructure cost.
- Partner with data scientists to define meaningful offline and production evaluation signals, including for use cases with delayed outcome labels.
- Use production evidence to improve model performance, platform reliability, deployment speed, and infrastructure efficiency.
- Build reusable training, evaluation, and deployment pipelines that support multiple models and modeling approaches.
- Develop dependable orchestration workflows covering dependencies, scheduling, retries, backfills, and failure recovery.
- Translate scientific requirements into maintainable production systems with clear interfaces, ownership, and operational expectations.
- Build and maintain an accessible model hub connecting model documentation, evaluation results, release history, versioning, and monitoring.
- Create tested CI/CD workflows, reproducible environments, actionable alerts, incident-response procedures, and operational runbooks.
- Define MLOps priorities in partnership with Data Science and Engineering, identifying risks before they become production issues.
- Establish reusable engineering standards, mentor teammates, and reduce the operational burden placed on data scientists.
Minimum Requirements
- 5+ years of experience building and operating production ML systems beyond initial deployment, including monitoring, retraining, incident response, and continuous improvement.
- Strong production software development skills in Python and proficiency with SQL.
- Hands-on experience with Spark/PySpark for large-scale distributed data processing, including feature engineering, historical aggregations, incremental processing, and training dataset construction.
- Experience with modern data lake or lakehouse architectures, including S3, Apache Iceberg, or equivalent technologies.
- Experience with a workflow orchestration engine for scheduling, dependency management, retries, backfills, and recovery.
- Hands-on experience building infrastructure with Terraform.
- Experience deploying and operating containerized production workloads on Kubernetes.
- Experience building reusable training, evaluation, or deployment infrastructure used by multiple models, scientists, or teams.
- Experience implementing model and dataset versioning, experiment tracking, lineage, and reproducible releases.
- Ability to distinguish service or infrastructure failures from changes in model effectiveness.
- Experience building CI/CD pipelines, monitoring, alerting, rollback mechanisms, and recovery procedures for production ML systems.
- Demonstrated ability to balance model quality, delivery speed, reliability, and infrastructure cost.
- Strong cross-functional communication and experience partnering with data scientists to preserve scientific validity as models move into production.
- Ability to explain a production incident or model degradation issue, how it was detected, and what improvements followed.
Preferred Requirements
- Experience developing platform services, infrastructure, or internal tooling in Go.
- Experience building or owning a model registry, model hub, or similar system used by technical and nontechnical stakeholders.
- Experience supporting ML systems where outcome labels are delayed or model quality cannot be measured immediately.
- Track record of improving deployment speed, model quality, system reliability, infrastructure cost, or manual operational effort.
- Experience designing configurable ML infrastructure that supports several modeling frameworks or approaches.
- Evidence that reusable infrastructure, standards, or documentation were adopted across multiple teams.
- Experience mentoring engineers or data scientists and influencing technical priorities across an ML portfolio.
- Experience working in environments with sensitive information and designing documentation or monitoring around appropriate access boundaries.
At Fetch, we offer competitive compensation packages including base, equity, and benefits to the exceptional folks we hire. The base salary range for this position is [$211,353 - $248,650]. Discover our benefits and how our employees live rewarded at https://fetch.com/careers.
At Fetch, we'll give you the tools to feel healthy, happy and secure through:
- Equity: We offer full-time employees equity in Fetch, so that everyone can benefit from Fetch’s growth.
- 401(k): Fetch will match your contributions dollar for dollar, up to 4% of your eligible pay.
- Benefits for humans and pets: We offer comprehensive medical, dental, and vision plans for everyone, including your pets.
- Continuing Education: Fetch provides ten thousand per year in education reimbursement.
- Employee Resource Groups: Take part in employee-led groups that are centered around fostering a diverse and inclusive workplace through events, dialogue, and advocacy. The ERGs participate in our Inclusion Council with members of executive leadership.
- Paid Time Off: On top of our flexible PTO, Fetch observes 9 paid holidays, as well as our year-end week-long break. (Holidays will vary for Canada-based roles)
- Robust Leave Policies: 14 weeks of fully paid parental leave for all employees, plus 6 additional weeks of paid childbirth recovery leave for birthing parents, and a flexible return-to-work schedule.
- Calvin Care Cash: Employees who are welcoming new family members will also receive a one-time $2,000 incentive to assist employees with covering the cost of childcare, clothing, diapers, and much more!
- Flexible Work Environment: Collaborate with your team in one of our stunning offices, or you can work fully remotely from anywhere in the US for US-based roles. We’ll ensure you are equally equipped with the hardware and software you need to get your job done in the comfort of your home. (applicable for most roles)
Fetch is an equal opportunity employer that embraces diversity, inclusion, and respect for all individuals. We do not discriminate on the basis of race, color, religion, gender, gender identity or expression, sexual orientation, age, national origin, marital status, veteran status, disability, or any other characteristic protected by applicable law. Our commitment to inclusivity ensures that everyone is treated with dignity and has the opportunity to succeed based on their talent, skills, and potential.
Fetch also provides reasonable accommodations to qualified individuals with disabilities or those with sincerely held religious beliefs, as required by law. If you need assistance with the application process or require an accommodation, please contact us at [email protected].
Skills Required
- 5+ years building and operating production machine learning systems beyond initial deployment, including monitoring, retraining, incident response, and continuous improvement
- Strong production software development skills in Python
- Proficiency with SQL
- Hands-on experience with Spark or PySpark for large-scale distributed data processing, feature engineering, historical aggregations, incremental processing, and training dataset construction
- Experience with modern data lake or lakehouse architectures, including S3, Apache Iceberg, or equivalent technologies
- Experience with a workflow orchestration engine for scheduling, dependency management, retries, backfills, and recovery
- Hands-on experience building infrastructure with Terraform
- Experience deploying and operating containerized production workloads on Kubernetes
- Experience building reusable training, evaluation, or deployment infrastructure used by multiple models, scientists, or teams
- Experience implementing model and dataset versioning, experiment tracking, lineage, and reproducible releases
- Ability to distinguish service or infrastructure failures from changes in model effectiveness
- Experience building CI/CD pipelines, monitoring, alerting, rollback mechanisms, and recovery procedures for production ML systems
- Ability to balance model quality, delivery speed, reliability, and infrastructure cost
- Strong cross-functional communication and experience partnering with data scientists to preserve scientific validity as models move into production
- Ability to explain a production incident or model degradation issue, how it was detected, and what improvements followed
- Experience developing platform services, infrastructure, or internal tooling in Go
- Experience building or owning a model registry, model hub, or similar system used by technical and nontechnical stakeholders
- Experience supporting ML systems with delayed outcome labels or quality measurements that are not immediately available
- Track record of improving deployment speed, model quality, system reliability, infrastructure cost, or manual operational effort
- Experience designing configurable ML infrastructure that supports several modeling frameworks or approaches
- Evidence that reusable infrastructure, standards, or documentation were adopted across multiple teams
- Experience mentoring engineers or data scientists and influencing technical priorities across an ML portfolio
- Experience working with sensitive information and designing documentation or monitoring around appropriate access boundaries
Fetch Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Fetch and has not been reviewed or approved by Fetch.
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Healthcare Strength — Benefits information points to comprehensive medical, dental, vision, and life insurance coverage. Feedback suggests employees value the strong health offerings alongside mental-health support.
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Leave & Time Off Breadth — Policies include flexible or unlimited PTO, paid holidays and sick days, bereavement (including pet) and natural-disaster leave. Feedback suggests time-off breadth is a standout element of the package.
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Wellbeing & Lifestyle Benefits — Wellness programs, 1:1 coaching, Gympass, team workouts, and nutrition counseling are provided. Office and remote perks such as home-office stipends, snacks, and pet-friendly spaces further enhance lifestyle support.
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The Company
What We Do
Through Fetch Points, the platform transforms everyday actions into rewarding moments, with more than $1 billion in Fetch Points awarded to date. Every day, more than 13 million receipts are submitted on Fetch, creating visibility into $212 billion in gross merchandise value and delivering the largest retail-agnostic, SKU-level view of household spending. This unmatched dataset powers Fetch as an outcomes-based advertising platform that acquires and retains lifelong consumers.
Why Work With Us
At Fetch, we back your ambition. You’ll have the scope, trust, and opportunity to turn bold ideas into meaningful impact for millions of people. Our builder-minded, high-agency, low-ego culture gives you room to shape what comes next and grow in ways that last, all while helping people live rewarded.
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