Staff ML Engineer, Fine Tuning - Slack

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Staff Machine Learning Engineer – Slack

Slack is looking for a Staff Machine Learning Engineer with deep expertise in model training and finetuning to join our ML team. You’ll design, train, and ship NLP models that power core product experiences — from summarization and search ranking to generative AI features used by millions daily. The role is hands‑on: you’ll work at a low level with training frameworks, optimize model architectures, build finetuning pipelines, and own the full lifecycle from experiment to production.

What you will be doing

• 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.

• Brainstorm with Product Managers, Designers and Frontend Engineers to conceptualize and build new features for our growing 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.

What you should have

• 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 such as PyTorch, TensorFlow, JAX, etc.

• Track record of shipping fine‑tuned models to production that serve real users at scale — not just research prototypes.

• Experience with functional or imperative programming languages: PHP, Python, Ruby, Go, C, Scala or Java.

• An analytical and data‑driven mindset, and know how to measure success with complicated ML/AI products.

• Led technical architecture discussions and helped drive technical decisions within the team.

• The ability to write understandable, testable code with an eye toward maintainability.

• Strong communication skills and the ability to explain complex technical concepts to designers, support, and other specialists.

Nice to have

• Expertise with recommendation systems or search.

• Familiarity with model optimization for inference (quantization, pruning, speculative decoding, compilation via 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.

Equal‑Opportunity Employment Statement

Salesforce is an equal‑opportunity employer and maintains a policy of non‑discrimination with all employees and applicants for employment. Any employee or potential employee will be assessed on the basis of merit, competence and qualifications – without regard to race, religion, color, national origin, sex, sexual orientation, gender expression or identity, transgender status, age, disability, veteran or marital status, political viewpoint, or other classifications protected by law.

Compensation

The typical base salary range for this position is $197,300 - $313,700 annually. In select cities within the San Francisco and New York City metropolitan area, the base salary range for this role is $237,700 - $344,700 annually. The range represents base salary only and does not include company bonus, incentive for sales roles, equity or benefits, as applicable.