What we’re building and why we’re building it Every month, millions of people use America’s Rewards App, earning rewards for buying brands they love – and a whole lot more. Whether shopping in the grocery aisle, grabbing a bite at the drive-through, or playing a favorite mobile game, Fetch empowers consumers to live rewarded throughout their day. To date, we’ve delivered more than $1 billion in rewards and earned more than 5 million five-star reviews from happy users. It’s not just our users who believe in Fetch: with investments from SoftBank, Univision, and Hamilton Lane, 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. About the Role Fetch is building the future of personalized consumer experiences. We’re looking for a Staff Machine Learning Engineer to serve as the technical lead for a high-impact ML team focused on personalization, relevance, and ranking. In this role, you will own the technical direction and execution for your team’s ML systems - driving high-quality architecture, guiding implementation, and ensuring models and infrastructure operate reliably at scale. You’ll partner closely with product and cross-functional stakeholders while remaining deeply hands-on in design and development. Role Responsibilities - Serve as the technical lead for a single ML-focused team, setting direction and raising the bar on engineering quality and system design. - Design, build, and scale ML systems supporting personalization, ranking, search, or ad-related use cases. - Own end-to-end architecture for your team’s services, including model training, evaluation, deployment, and serving. - Drive clarity in ambiguous problem spaces, translating product needs into scalable technical solutions. - Lead design reviews and ensure thoughtful tradeoffs around latency, reliability, experimentation, and maintainability. - Partner closely with product, data, and engineering stakeholders to deliver measurable business impact. - Mentor engineers through hands-on technical guidance, feedback, and example. - Use AI tools to accelerate development and improve system design, including: - Prototyping and validating ideas with LLM tools. - Leveraging AI for code iteration and experimentation. - Using AI assistants for architecture diagramming and design validation. - Exploring LLM-powered features where appropriate. Minimum Requirements - 8+ years of industry experience in machine learning or software engineering, with demonstrated ownership of production ML systems operating at scale. - Proven experience building and scaling ML systems in personalization, relevance, search, or ad tech domains. - Strong hands-on expertise in distributed systems, data pipelines, and ML infrastructure. - Experience deploying ML models into production and operating them at consumer scale. - Demonstrated ownership of complex technical initiatives within a team. - Strong systems design skills with the ability to clearly articulate tradeoffs and implementation decisions. - Experience mentoring engineers and influencing technical standards within a team. - Ability to operate effectively in ambiguous environments and drive projects to completion. Preferred Requirements - Familiarity with LLMs and their application in personalization, feature generation, or search. - Experience with real-time or streaming ML systems. - Exposure to experimentation frameworks (A/B testing) and model performance measurement. - Experience bridging model development with real-time serving systems. This is a full-time role that can be held from one of our US offices or remotely in the United States. Compensation: At Fetch, we offer competitive compensation packages including base, equity, and benefits to the exceptional folks we hire. Discover our benefits and how our employees live rewarded at https://fetch.com/careers.

• Lead and oversee the strategy development, quarterly planning and day-to-day execution of initiatives related to ads marketplace, auction and pricing. • Proactively further our understanding of marketplace dynamics and develop algorithms to improve the efficiency and effectiveness of our ads marketplace, auction and pricing. • Oversee end-to-end ML workflows—from data ingestion and feature engineering to model training, evaluation, and deployment—that optimizes the ads marketplace efficiency. • Be a mentor, lead both junior and senior engineers in implementing technical designs and reviews. Fostering a culture of innovation, technical excellence, and knowledge sharing across the organization. • Be a cross-functional advocate for the team, collaborate with cross-functional teams (e.g., product management, data science, PMM, Sales etc.) to innovate and build products.

remote / rewrite monolith to microservices This Jobot Job is hosted by: Katrina McFillin Are you a fit? Easy Apply now by clicking the "Apply" button and sending us your resume. Salary: $170,000 - $210,000 per year A bit about us: We are a fintech platform specializing in auto loan refinancing. Why join us? We offer comprehensive benefits including 401(k) matching, company equity, health insurance, generous parental leave, and remote work options. Employees also appreciate flexible schedules, professional development opportunities, and a workplace culture that values diversity, inclusion, and volunteering within the local community. Job Details You will be joining a team of 8 engineers in a mid size organization managing a large monolith. Tech stack: React, Ruby on Rails, PostgresSQL, and AWS. This is a fully remote position Interested in hearing more? Easy Apply now by clicking the "Apply" button. Jobot is an Equal Opportunity Employer. We provide an inclusive work environment that celebrates diversity and all qualified candidates receive consideration for employment without regard to race, color, sex, sexual orientation, gender identity, religion, national origin, age (40 and over), disability, military status, genetic information or any other basis protected by applicable federal, state, or local laws. Jobot also prohibits harassment of applicants or employees based on any of these protected categories. It is Jobot’s policy to comply with all applicable federal, state and local laws respecting consideration of unemployment status in making hiring decisions. Sometimes Jobot is required to perform background checks with your authorization. Jobot will consider qualified candidates with criminal histories in a manner consistent with any applicable federal, state, or local law regarding criminal backgrounds, including but not limited to the Los Angeles Fair Chance Initiative for Hiring and the San Francisco Fair Chance Ordinance. Information collected and processed as part of your Jobot candidate profile, and any job applications, resumes, or other information you choose to submit is subject to Jobot's Privacy Policy, as well as the Jobot California Worker Privacy Notice and Jobot Notice Regarding Automated Employment Decision Tools which are available at jobot.com/legal. By applying for this job, you agree to receive calls, AI-generated calls, text messages, or emails from Jobot, and/or its agents and contracted partners. Frequency varies for text messages. Message and data rates may apply. Carriers are not liable for delayed or undelivered messages. You can reply STOP to cancel and HELP for help. You can access our privacy policy here: jobot.com/privacy-policy

• Design and train supervised and unsupervised models • Develop NLP and LLM solutions • Perform fine-tuning of foundational models • Implement RAG (Retrieval-Augmented Generation) architectures • Create training, validation, and inference pipelines • Evaluate model performance (accuracy, F1, ROC, BLEU, etc.) • Implement monitoring for drift and performance degradation • Collaborate with engineering teams to deploy models to production • Manage computational costs for training and inference