Lead Machine Learning Engineer Search & Recommendations
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2 hours ago
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1 Vacancy
Job Summary
Ambi is partnering with Upwork to support the hiring of key talent as they build out their new international hub.
Upwork Inc.s family of companies connects businesses with global AI-enabled talent across every contingent work type including freelance fractional and payrolled. This portfolio includes the Upwork Marketplace which connects businesses with on-demand access to highly skilled talent across the globe and Lifted which provides a purpose-built solution for enterprise organizations to source contract manage and pay talent across the full spectrum of contingent work. From Fortune 100 enterprises to entrepreneurs businesses rely on Upwork Inc. to find and hire expert talent leverage AI-powered work solutions and drive business transformation. With access to professionals spanning more than 10000 skills across AI & machine learning software development sales & marketing customer support finance & accounting and more the Upwork family of companies enables businesses of all sizes to scale innovate and transform their workforces for the age of AI and beyond.
Since its founding Upwork Inc. has facilitated more than $30 billion in total transactions and services as it fulfills its purpose to create opportunity in every era of work. Learn more about the Upwork Marketplace at and follow us on LinkedIn Facebook Instagram TikTok and X; and learn more about Lifted at Go-Lifted and follow on LinkedIn.
Lead Machine Learning Engineer Search and Recommendations
Were looking for a Lead Machine Learning Engineer to build personalized memory systems for Search and Recommendations enabling models to better understand user intent preferences and evolving needs across interactions.
This role sits at the intersection of memory modeling retrieval ranking and personalization with a primary focus on learning and applying personalized memory representations rather than building general-purpose memory infrastructure. You will design how memory signals are encoded updated decayed and surfaced to influence candidate retrieval ranking and personalization decisions across the marketplace.
As a Lead-level individual contributor you will own complex technical initiatives work closely with engineering research product and data partners and translate personalized memory concepts into robust measurable production-ready machine learning systems that improve relevance engagement and hiring outcomes.
Responsibilities
- Design and build personalized memory systems for Search and Recommendations that improve understanding of user intent preferences and behavioral evolution.
- Develop user- session- and interaction-level memory representations that directly inform candidate retrieval ranking and personalization decisions.
- Integrate memory-driven signals into retrieval and ranking pipelines to improve relevance engagement and downstream hiring outcomes.
- Model temporal dynamics of user behavior including recency frequency decay and preference drift translating them into stable high-impact personalization features.
- Train and evaluate memory-aware ranking and personalization models using offline relevance metrics and online experimentation frameworks.
- Partner with conversational and LLM-assisted search teams to support context-aware query understanding while maintaining focus on search relevance and ranking quality.
- Productionize memory-driven ML systems with an emphasis on latency scalability observability and experimentation rigor.
- Provide technical leadership through design reviews mentorship and shared best practices for building scalable personalization systems.
What it takes to catch our eye
- Demonstrated experience building and deploying search or recommendation systems in production with measurable impact on relevance engagement or conversion metrics.
- Strong foundation in retrieval and ranking systems including candidate generation re-ranking and offline and online evaluation techniques.
- Practical experience modeling personalization and behavioral memory including user intent preferences temporal dynamics and signal tradeoffs.
- Solid machine learning engineering skills across the full lifecycle including pipelines experimentation deployment and inference at scale.
- An adaptive approach to integrating AI tools into modeling and engineering workflows to accelerate experimentation improve quality and support team learning.
- Comfort operating in ambiguity with the ability to define open-ended problems design experiments and iterate based on data.
- Bonus experience contributing to applied research publications or experimentation in search recommendation or applied machine learning.
This position will initially be employed through a partner to ensure a seamless hiring process while Upwork establishes its new hub. While employed through this structure you will work as part of Upworks team with access to its resources culture and growth opportunities. Once the hub is established there may be opportunities to transition to direct employment with Upwork depending on business needs and other requirements.
