Why This Job is Featured on The SaaS Jobs
This Data Scientist II role is notable in today’s SaaS landscape because it sits at the point where generative AI moves from experimentation into production software. The emphasis on fine tuning foundation models, working with large datasets, and deploying through MLOps practices signals work that directly affects how SaaS products differentiate, automate workflows, and improve user outcomes at scale.
For a SaaS career, the durable value here is the combination of model development and operational rigor. Experience with experiment tracking, model versioning, CI CD for ML pipelines, and cloud scale training builds an end to end skill set that translates across AI enabled SaaS companies. Exposure to distributed training frameworks and GPU optimization also maps to the practical constraints SaaS teams face when balancing latency, cost, and quality in real customer environments.
This position tends to suit practitioners who prefer applied problem solving over purely research oriented work, and who can collaborate across product and engineering to land measurable improvements. It also fits someone ready to take on technical ownership while still remaining hands on, with a willingness to mentor or guide others as the work matures into repeatable systems.
The section above is editorial commentary from The SaaS Jobs, provided to help SaaS professionals understand the role in a broader industry context.
Job Description
Technical Expertise:
- Strong background in machine learning, deep learning, and NLP, with proven experience in training and fine-tuning large-scale models (LLMs, transformers, diffusion models, etc.).
- Hands-on expertise with Parameter-Efficient Fine-Tuning (PEFT) approaches such as LoRA, prefix tuning, adapters, and quantization-aware training.
- Proficiency in PyTorch, TensorFlow, Hugging Face ecosystem and good to have distributed training frameworks (e.g., DeepSpeed, PyTorch Lightning, Ray).
- Basic understanding of MLOps best practices, including experiment tracking, model versioning, CI/CD for ML pipelines, and deployment in production environments.
- Experience working with large datasets, feature engineering, and data pipelines, leveraging tools such as Spark, Databricks, or cloud-native ML services (AWS Sagemaker, GCP Vertex AI or Azure ML).
- Knowledge of GPU/TPU optimization, mixed precision training, and scaling ML workloads on cloud or HPC environments.
Applied Problem-Solving:
- Mandatory skill - Demonstrated success in adapting foundation models to domain-specific applications through fine-tuning or transfer learning.
- Mandatory skill - Strong ability to design, evaluate, and improve models using robust validation strategies, bias/fairness checks, and performance optimization techniques.
- Experience in working on applied AI problems across NLP, computer vision, or multimodal systems or any other domain.
Leadership & Collaboration:
- (Preferred) Proven ability to lead and mentor a junior applied scientists and ML engineers, providing technical guidance and fostering innovation.
- Strong cross-functional collaboration skills to work with product, engineering, and business stakeholders to deliver impactful AI solutions.
- Ability to translate cutting-edge research into practical, scalable solutions that meet real-world business needs.
Education & Experience:
- 3+ years of hands-on experience in applied machine learning and data science with Master’s or Ph.D. in Computer Science, Machine Learning, Data Science, Statistics, or a related field or appropriate experience.
- Excellent communication and presentation skills to articulate complex ML concepts to both technical and non-technical audiences.
- Continuous learner with awareness of emerging trends in generative AI, foundation models, and efficient ML techniques.
