Staff ML Engineer, Fine Tuning - Slack

About the Company Slack is a collaboration platform that helps teams communicate and build high‑impact products. The company is dedicated to delivering reliable, machine‑learning powered features to millions of users around the world. Position Overview Slack is looking for a **Staff Machine Learning Engineer** with deep expertise in model training and fine‑tuning. The role focuses on designing, building, and shipping production‑ready NLP models that enable features such as summarization, search ranking, and generative AI. The Engineer will own the full lifecycle of model development—from data curation and training to deployment and monitoring—ensuring robustness and scalability. Responsibilities Design and execute finetuning strategies for large language models and other deep‑learning architectures tailored to Slack’s NLP tasks (summarization, ranking, classification, generation). Own the model training lifecycle end‑to‑end: data curation, training infrastructure, hyperparameter optimization, evaluation, deployment and monitoring. Build and maintain scalable finetuning training pipelines on GPU infrastructure. Collaborate with Product Managers, Designers and Frontend Engineers to conceptualize and build new features for Slack’s growing user base. Lead or contribute heavily to large, multi‑functional projects that have significant business impact. Mentor other engineers and conduct thorough code reviews. Improve engineering standards, tooling, and processes. Qualifications 5+ years of hands‑on experience training and fine‑tuning deep‑learning models in NLP (or a closely related domain such as speech, information retrieval, or multimodal). 5+ years of experience with major deep‑learning frameworks such as PyTorch, TensorFlow, or JAX. Track record of shipping fine‑tuned models to production at scale, serving real users, not just research prototypes. Experience with functional or imperative programming languages: PHP, Python, Ruby, Go, C, Scala, or Java. Analytical, data‑driven mindset and ability to measure success across complex ML/AI products. Led technical architecture discussions and helped drive technical decisions within the team. Strong communication skills; able to explain complex technical concepts to designers, support staff, and other specialists. Nice to Have Expertise with recommendation systems or search applications. Familiarity with model optimization for inference (quantization, pruning, speculative decoding, TorchScript/TensorRT/ONNX). Experience with retrieval‑augmented generation and hybrid retrieval/generation systems. Broad experience across NLP, ML, and generative AI capabilities. Knowledge of using multiple data types in RAG solutions, including structured, unstructured, and knowledge graphs. Benefits Competitive base salary (range $197,300 – $344,700, depending on location). Time‑off programs, paid parental leave, and flexible working arrangements. Medical, dental, vision, and mental health support. Life and disability insurance. 401(k) plan with company match and employee stock purchasing program. Additional benefits and perks, including wellness resources, on‑site amenities, and professional development support. More details at . Equal Opportunity Statement Salesforce is an equal‑opportunity employer and maintains a policy of non‑discrimination with all employees and applicants for employment. We believe in equality for all and create an inclusive workplace free from discrimination based on race, religion, color, national origin, sex, sexual orientation, gender identity or expression, transgender status, age, disability, veteran or marital status, political viewpoint, or any other protected classification. Recruiting, hiring, and promotion decisions are made based on merit, competence, and qualifications. #J-18808-Ljbffr 100 Salesforce, Inc.