Staff Machine Learning Engineer (Fine Tuning, Slack)

Join Slack as a Staff Machine Learning Engineer, where you'll design, train, and ship NLP models that power core product experiences. This hands-on role involves optimizing model architectures, building finetuning pipelines, and owning the full lifecycle from experiment to production. You'll work closely with Product Managers, Designers, and Frontend Engineers to conceptualize and build new features for our growing user base.

• Concevoir, entraîner et expédier des modèles de traitement du langage naturel (NLP) qui alimentent les expériences produit.
• Posséder le cycle de vie de l'entraînement des modèles de bout en bout : curation des données, infrastructure d'entraînement, optimisation des hyperparamètres, évaluation, déploiement et surveillance.
• Diriger ou contribuer de manière significative à de grands projets multifonctionnels qui ont un impact significatif sur l'entreprise.
• 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
• 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
• Led technical architecture discussions and helped drive technical decisions within the team
• 5+ years of experience with common deep learning frameworks like PyTorch, TensorFlow, JAX, etc
• Experience with functional or imperative programming languages: PHP, Python, Ruby, Go, C, Scala or Java
• The ability to write understandable, testable code with an eye towards maintainability
• Track record of shipping fine-tuned models to production that serve real users at scale — not just research prototypes
• 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)
• An analytical and data driven mindset, and know how to measure success with complicated ML/AI products
• Strong communication skills and you are capable of explaining 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 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