ML Engineer, Fine Tuning - Slack

Responsibilities

• 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 and 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.

• 5+ years of hands‑on experience training and fine‑tuning deep learning models in NLP (or a closely related domain such as 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, with a focus on measuring 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 ability 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.

In the United States, compensation offered will be determined by factors such as location, job level, job‑related knowledge, skills, and experience. Certain roles may be eligible for incentive compensation, equity, and benefits. Salesforce offers a variety of benefits to help you live well, including time off programs, medical, dental, vision, mental health support, paid parental leave, life and disability insurance, 401(k), and an employee stock purchasing program.

More details about company benefits can be found at  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.

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.

The same goes for compensation, benefits, training, and promotion decisions.