SoftBank Vision Fund Staff Machine Learning Engineer - Ad Platform
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Summary
Technical lead for a machine learning team at Fetch building personalization, ranking, and ad-relevance systems — owning end-to-end architecture from model training and evaluation to production serving. Requires deep distributed-systems and ML infrastructure experience; role is remote in the US or Canada (excluding Quebec).
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
- 12+ 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.
- Bachelor’s degree in Computer Science, Engineering, or a related technical field.
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 or any Canadian province (excluding Quebec), subject to appropriate work authorization. Canadian employment, if applicable, would be through Fetch’s Canadian Employer of Record.
Benefits for Canada-based employees may differ slightly to comply with applicable law(s) (e.g., paid time off, Holidays, etc.).
Fetch may use technology tools that incorporate artificial intelligence ("AI") capabilities to support certain activities within our recruiting and hiring process:
- Reviewing and organizing applications and resumes to help our recruiting team manage high volumes of submissions, including automated review of fraud signals to identify bad actors;
- Identifying candidates whose stated qualifications and experience appear relevant to an open position;
- Scheduling and coordinating interviews with candidates;
- Generating interview summaries or notes to assist recruiters and hiring managers in their review; and
- Conducting research on open roles and candidate pools to improve the effectiveness of our recruiting practices.
Fetch does not use AI tools to make hiring decisions. All decisions regarding whether to advance a candidate, extend an offer, or decline an application are made by Fetch employees, who are predominantly members of our Talent Acquisition team and relevant Hiring Managers. AI tools serve only to assist the process of recruiting talent and to inform the human review of candidate information. In no way does Fetch allow AI systems to make, finalize, or override any employment decision
