Upwork ($UPWK) is the world’s work marketplace. We serve everyone from one-person startups to over 30% of the Fortune 100 with a powerful, trust-driven platform that enables companies and talent to work together in new ways that unlock their potential.
Last year, more than $3.3 billion of work was done through Upwork by skilled professionals who are gaining more control by finding work they are passionate about and innovating their careers.
This is an engagement through Upwork’s Hybrid Workforce Solutions (HWS) Team. Our Hybrid Workforce Solutions Team is a global group of professionals that supports Upwork’s business. Our HWS team members are located all over the world.
Work/Project Scope:
As a Machine Learning Engineer, you will play a crucial role in developing and maintaining discriminative and generative AI models. You will design, train/finetune, and evaluate information retrieval systems capable of handling both structured and unstructured databases. Leveraging historical context and diverse data, you will create innovative models to drive significant business impact.
Key Responsibilities:
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- Train/Finetune large language models for query understanding and tool calling.
- Develop and utilize vector databases and/or knowledge graphs for information retrieval. Orchestrate information retrieval using databases in the loop.
- Investigate model explainability to enhance reliability and reduce hallucination in end-to-end Uma use cases.
- Collaborate cross-functionally with machine learning engineers to take models from research to production.
- Stay current with the latest advancements in Gen-AI and integrate relevant innovations into our systems.
- Publish paper in top-tier conference. Mentor junior engineers, conduct code reviews, and enforce engineering best practices.
- Deliver high-quality solutions that improve overall efficiency.
Required Skills and Qualifications:
- Deep Learning/Artificial Intelligence (AI): 5+ years of hands-on experience designing, training, and deploying deep learning models, specifically in areas related to Natural Language Processing (NLP) and Information Retrieval.
- Large Language Model (LLM) Finetuning/Post-Training: Proven experience in finetuning or pre-training LLMs (e.g., Llama, GPT family, Mistral) for specific downstream tasks like query understanding and instruction following.
- Pytorch: Expert-level proficiency in PyTorch for building, training, and optimizing complex deep learning models.
- Function Calling/Tool Calling/Structured Decoding: Direct experience developing and implementing models capable of structured output generation (e.g., JSON, YAML) and tool/function calling (giving the model the ability to interact with external systems or databases).
- Agentic Workflow Development: Experience in designing, implementing, and orchestrating complex AI Agents or multi-step reasoning workflows using LLMs.
- Reinforcement Learning (RL) / Reinforcement Learning from Human Feedback (RLHF): Practical knowledge of applying RL principles, particularly in the context of RLHF, for aligning LLMs with human preferences, improving model safety, and enhancing instruction-following capabilities.
Upwork is proudly committed to fostering a diverse and inclusive workforce. We never discriminate based on race, religion, color, national origin, gender (including pregnancy, childbirth, or related medical condition), sexual orientation, gender identity, gender expression, age, status as a protected veteran, status as an individual with a disability, or other applicable legally protected characteristics.
Additionally, a criminal background check may be run on a candidate after a conditional offer to perform your services for Upwork 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.
