Lead Machine Learning Engineer/Scientist, Algorithms & Research at Upwork Global Inc

Lisbon, , Portugal -

Full Time

Start Date

Immediate

Expiry Date

09 Apr, 26

Salary

130000.0

Posted On

09 Jan, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine Learning, Dynamic Memory Management, RAG Systems, Memory Architectures, Function Calling, Tool Calling, Fine-Tuning, Evaluation Methodologies, Ranking Systems, Retrieval Modeling, Software Engineering, Data Pipelines, Cross-Functional Collaboration, Mentoring, Code Reviews, Personalization

Industry

Software Development

Description

Lead Machine Learning Engineer/Scientist - Algorithms & Research Upwork Inc.'s (Nasdaq: UPWK) 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 10,000 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 Upwork.com and follow us on LinkedIn, Facebook, Instagram, TikTok, and X; and learn more about Lifted at Go-Lifted and follow on LinkedIn. We’re looking for a Lead Machine Learning Engineer/Scientist to help build a Dynamic Memory Management capability for our LLM-powered experiences (including agentic systems and tool-using assistants). This role sits at the intersection of retrieval, memory, reasoning, and orchestration - designing how AI systems store, update, compress, retrieve, and apply knowledge across sessions, tasks, and workflows. In this role, you’ll focus on building production-grade memory architectures that integrate structured and unstructured signals, including user preferences, entities, constraints, conversation history, tool results, marketplace context, and long-term facts. You’ll design “memory policies” (what to store, when to forget, how to summarize), develop RAG + memory fusion strategies, and train or post-train models to reliably execute function calls/tool calls grounded in memory and context. You’ll partner closely with engineering, research, product, and trust & safety teams to transition memory research prototypes into robust, measurable, production-ready systems, improving personalization, reducing hallucinations, increasing task success rates, and enabling more capable autonomous workflows. Responsibilities Design and build a Dynamic Memory Management system for LLM/agent applications, including memory ingestion, CRUD operations, retrieval, consolidation, summarization, and forgetting policies. Develop RAG + memory architectures that integrate vector databases, relational databases, and knowledge graph-based representations for context-aware reasoning. Create ranking and retrieval strategies (e.g., multi-stage retrieval, re-ranking, memory salience scoring, recency vs. importance tradeoffs, conflict resolution, deduplication). Build and evaluate pipelines and models (retrieval-augmented context/knowledge flows), including modeling for memory selection and grounding. Train, fine-tune, and post-train models for reliable function calling / tool calling, including: tool selection, schema adherence, multi-step tool plans post-training with preference optimization / RL-style methods or constrained decoding approaches safety-aware and policy-compliant tool use Experience leveraging discriminator/verifier models (or LLM-as-judge frameworks) to assess memory correctness, grounding quality, hallucination risk, and tool-call validity. Establish offline + online evaluation: memory precision/recall, factual consistency, task success rate, latency/cost, and long-term personalization impact. Lead cross-functional efforts to ship memory capabilities end-to-end: data pipelines, privacy boundaries, storage, retrieval, orchestration, monitoring, and iteration loops. Mentor engineers/researchers, conduct detailed code reviews, and set best practices for building scalable LLM + retrieval systems. Deliver high-quality, measurable outcomes aligned with team and organizational goals. What it takes to catch our eye Proven track record building and deploying LLM-powered agent systems in production with measurable business impact. Strong experience with RAG systems, including retrieval pipelines, embedding strategies, re-ranking, and evaluation methodologies. Deep practical understanding of memory in LLM systems: long/short-term memory, summarization, consolidation, forgetting, conflict resolution, and personalization. Hands-on experience with function calling/tool calling systems and post-training models to reliably invoke tools in structured formats. Experience with fine-tuning and post-fine-tuning (e.g., supervised fine-tuning, preference optimization, RL-style approaches), including dataset construction and training pipelines. Familiarity with discriminator/verifier models, reward modeling, or automated evaluation strategies for grounding and tool correctness. Strong foundations in ranking systems, retrieval modeling, and/or representation learning (dense + sparse + hybrid retrieval). Comfortable operating in ambiguity: can define the problem, design experiments, ship incrementally, and improve iteratively with rigorous measurement. Excellent software engineering skills: clean code, strong testing discipline, scalable systems mindset. Strong publication record in top-tier ML conferences like NeurIPS, ICML, ICLR, CVPR etc. Come change how the world works. At Upwork, you’ll shape the future of work for a global, remote-first workforce, creating economic opportunities for professionals worldwide. While we have a physical office in Palo Alto, we currently hire full-time employees in 21 U.S. states, making it easier than ever to join our mission from wherever you call home. Our culture is built on trust, risk-taking, customer focus, and excellence, all in service of our core mission: to create economic opportunities so people have better lives. We embrace authenticity and inclusion, encouraging everyone to bring their whole selves to work. Personal and professional growth is a priority here, supported through development programs, mentorship, and our Upwork Belonging Communities. We’re proud to offer benefits that go beyond the basics, including comprehensive medical coverage for you and your family, unlimited PTO, a 401(k) plan with matching, 12 weeks of paid parental leave, and an Employee Stock Purchase Plan. Visit our Life at Upwork page to learn more about our values, working principles, and the overall employee experience. Ready to help shape the future of work? Check out our Careers page and follow us on LinkedIn, Facebook, Instagram, TikTok, and X to learn more about life at Upwork. Upwork is an Equal Opportunity Employer committed to recruiting and retaining a diverse and inclusive workforce. We do not discriminate based on race, religion, color, national origin, gender (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender identity, gender expression, age, status as a protected veteran, status as an individual with a disability, genetic information, or other legally protected characteristics under federal, state, or local law. Please note that a criminal background check may be required once a conditional job offer is made. Qualified applicants with arrest or conviction records will be considered in accordance with applicable law, including the California Fair Chance Act and local Fair Chance ordinances. The annual base salary range for this position is displayed below. The range displayed reflects the minimum and maximum salary for this position, and individual base pay will depend on your skills, qualifications, experience, and location. Additionally, this position is eligible for the annual bonus plan or sales incentive plan and eligibility to participate in our long term equity incentive program. Annual Base Compensation €75.000—€130.000 EUR Please note that a criminal background check may be required once a conditional job offer is made. Qualified applicants with arrest or conviction records will be considered in accordance with applicable law, including the California Fair Chance Act and local Fair Chance ordinances. The Company is committed to conducting an individualized assessment and giving all individuals a fair opportunity to provide relevant information or context before making any final employment decision. To learn more about how Upwork processes and protects your personal information as part of the application process, please review our Global Job Applicant Privacy Notice

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Responsibilities

Design and build a Dynamic Memory Management system for LLM/agent applications, focusing on memory ingestion and retrieval. Lead cross-functional efforts to transition memory research prototypes into production-ready systems.