Senior Lead Machine Learning Engineer, Agentic AI

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.

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We’re seeking a Senior Lead Machine Learning Engineer to architect, ship, and scale the next generation of agentic intelligence across Upwork. You will lead end‑to‑end development of AI agents and the platform that powers them—from LLM training and evaluation to runtime orchestration, safety, and developer APIs. This is a hands‑on, high‑impact role at the intersection of applied research and platform engineering, enabling internal teams and external developers to build reliable, safe, and high‑performing agents on Upwork.

Responsibilities

• Build Agentic Intelligence.  Design and implement multi‑agent systems (planning, tool‑use, memory, debate/critique, reflection) with robust guardrails and recovery strategies.
• Develop protocol‑aware agents and services that interoperate cleanly with developer tooling (e.g., agent frameworks and protocols such as MCP). 
• Own reliability at scale: deterministic execution where needed, idempotency, timeouts/retries, and evaluation‑driven iteration on agent behavior.
• Train, Align, and Evaluate LLMs for Agents.  Lead data strategy and curation for agent tasks; drive SFT, DPO, RLHF/RLAIF, and safety tuning tailored to multi‑tool, multi‑step workflows.
• Stand up evaluation harnesses for functional, task, and longitudinal metrics (success rate, time‑to‑completion, hallucination/escape rates, cost/latency).
• Build policy‑driven guardrails; partner with Legal/Security on data governance and privacy.
• Engineer Agentic Platform Backend Infrastructure.  Architect low‑latency inference, retrieval, and orchestration services (streaming, event‑driven pipelines; scalable queues; caching; batching) with strong SLOs.
• Ship production‑grade services (APIs/SDKs, auth, rate limiting, observability) that make agent features easy to integrate for internal and external developers. 
• Optimize cost/performance via quantization, distillation, model‑routing, and autoscaling; integrate evaluation signals directly into runtime and CI/CD.
• Lead, Partner, and Uplevel the Ecosystem.  Provide technical leadership across research, product, and platform teams; mentor senior ICs; influence roadmaps with clear metrics and trade‑offs.
• Publish internal guidance and exemplar implementations; contribute to technical content, samples, and reference architectures for our agent platform. 
• Define and track KPIs for data/quality/throughput, and drive continuous improvement using experiment results and production telemetry.

What it takes to catch our eye

• 8–12+ years in appli