Staff ML Engineer, Fine Tuning - Slack

Staff Machine Learning Engineer

To get the best candidate experience, please consider applying for a maximum of 3 roles within 12 months to ensure you are not duplicating efforts.

Software Engineering

Salesforce is the #1 AI CRM, where humans with agents drive customer success together. Here, ambition meets action. Tech meets trust. And innovation isn't a buzzword — it's a way of life. The world of work as we know it is changing and we're looking for Trailblazers who are passionate about bettering business and the world through AI, driving innovation, and keeping Salesforce's core values at the heart of it all.

Ready to level-up your career at the company leading workforce transformation in the agentic era? You're in the right place! Agentforce is the future of AI, and you are the future of Salesforce.

*IN SCHOOL OR GRADUATED WITHIN THE LAST 12 MONTHS? PLEASE VISIT FUTURE FORCE FOR OPPORTUNITIES*

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. This 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.

At Slack, that impact can be huge:

• We have over 10 million daily active users relying on our product.
• At peak usage, a million messages a minute pass through Slack.
• During the week, our users spend over a billion minutes a day active in our product.

Machine learning engineers at Slack ship models that serve millions of users daily. This role owns that end-to-end: finetuning models for Slack's NLP tasks and putting them into production with the rigor and reliability our users expect. We're not looking for someone who hands off a checkpoint — we want someone who sees it through to serving traffic. Broader ML skills — data pipelines, experimentation, feature engineering — are valuable here too, but deep training and productionization expertise is the core of this role.This is a practical machine learning team, not a research team. Our goal is to deliver business value with machine learning and data in whatever form that takes. Sometimes that means bootstrapping something simple like a logistic regression and moving on. Other times that means developing sophisticated, finely tuned models and novel solutions to Slack's unique problem space. We are looking for engineers who are driven by driving impact for our business, building great products for our customers, and delivering robust, reliable services with machine learning.

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 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.

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 like 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 towards maintainability.
• Strong communication skills and you are capable of explaining 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.

Unleash Your Potential

When you join Salesforce, you'll be limitless in all areas of your life. Our benefits and resources support you to find balance and be your best, and our AI agents accelerate your impact so you can do your best . Together, we'll bring the power of Agentforce to organizations of all sizes and deliver amazing experiences that customers love. Apply today to not only shape the future — but to redefine what's possible — for yourself, for AI, and the world.

Accommodations

If you need a reasonable accommodation during the application or the recruiting process, please submit a request via this Accommodations Request Form .

Posting Statement

Salesforce is an equal opportunity employer and maintains a policy of non-discrimination with all employees and applicants for employment. What does that mean exactly? It means that at Salesforce, we believe in equality for all. And we believe we can lead the path to equality in part by creating a workplace that's inclusive, and free from discrimination. Know your rights: workplace discrimination is illegal. 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. This policy applies to current and prospective employees, no matter where they are in their Salesforce employment journey. It also applies to recruiting, hiring, job assignment, compensation, promotion, benefits, training, assessment of job performance, discipline, termination, and everything in between. Recruiting, hiring, and promotion decisions at Salesforce are fair and based on merit. The same goes for compensation, benefits, promotions, transfers, reduction in workforce, recall, training, and education.

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 following link: to the San Francisco Fair Chance Ordinance and the Los Angeles Fair Chance Initiative for Hiring, Salesforce will consider for employment qualified applicants with arrest and conviction records.At Salesforce, we believe in equitable compensation practices that reflect the dynamic nature of labor markets across various regions.

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.