ML Engineer, Fine Tuning - Slack

Job Overview

Slack is looking for a Staff Machine Learning Engineer with deep expertise in model training and fine tuning to join our ML team. The role involves designing, training, and shipping NLP models that power core product experiences such as summarization, search ranking, and generative AI features used by millions daily. You will work at a low level with training frameworks, optimize model architectures, build fine tuning pipelines, and own the full lifecycle from experiment to production.

• Over 10 million daily active users rely on Slack.
• Peak usage includes a million messages per minute.
• Users spend over a billion minutes a day active in our product.

• Design and execute fine tuning 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 fine tuning training pipelines on GPU infrastructure.
• Brainstorm with Product Managers, Designers and Frontend Engineers to conceptualize and build new features for our large user base.
• Produce high‑quality results by leading or contributing heavily to large multi‑functional projects that have a significant impact on the business.
• Mentor other engineers and deeply review code.
• Improve engineering standards, tooling, and processes.

• 5+ years of hands‑on experience training and fine‑tuning deep learning models in NLP (or a closely related domain like speech, IR, or multimodal).
• 5+ years of experience with common deep learning frameworks like PyTorch, Tensor Flow, JAX, etc.
• Track record of shipping fine‑tuned models to production that serve real users at scale.
• Experience with functional or imperative programming languages: PHP, Python, Ruby, Go, C, Scala or Java.
• Analytical and data‑driven mindset, and ability to measure success with complex ML/AI products.
• Led technical architecture discussions and helped drive technical decisions within the team.
• Ability to write understandable, testable code with an eye toward maintainability.
• Strong communication skills and capability to explain complex technical concepts to designers, support, and other specialists.

• Expertise with recommendation systems or search.
• Familiarity with model optimization for inference (quantization, pruning, speculative decoding, compilation via Torch Script/Tensor
  
  RT/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.

Base salary range: $197,300 – $313,700 annually (typical). In selected cities within the San Francisco and New York City metropolitan areas, range: $237,700 – $344,700 annually.

Salesforce is an equal‑opportunity employer and maintains a policy of non‑discrimination with all employees and applicants for employment. Recruitment, hiring, and promotion decisions are fair and based on merit.