What You’ll Do
The Field Engineering team at Weights & Biases plays a vital role in ensuring customer success and adoption of our platform. As part of this team, we partner with Sales, Support, Product, and Engineering to lead technical success after the sales process. We work closely with some of the most advanced AI teams in the world, helping them build, optimize, and scale their ML and GenAI workflows across industries such as computer vision, robotics, natural language processing, and large language models (LLMs).

About the Role

We’re hiring a Post-Sales AI Solutions Engineer (AISE), Scale Customer Success to help customers successfully implement and scale AI/ML workflows and GenAI/agentic applications on Weights & Biases. You will design and deliver technical enablement and adoption programs that reach many customers at once, create reusable assets that improve self-serve success, and use product signals and feedback loops to continuously improve outcomes.

Key Responsibilities 

1. Run 1-to-many onboarding and enablement programs
   Own and deliver scalable onboarding and adoption motions (webinars, cohort sessions, group training, and office hours) that help customers get to value quickly and consistently.
2. Build reusable technical assets that drive self-serve success
   Create and maintain playbooks, reference architectures, templates, sample code/notebooks, and troubleshooting guides that standardize best practices and reduce repeated 1:1 work.
3. Operate the scaled motion using signals and feedback loops
   Use product usage signals and customer patterns to segment and trigger the right interventions, track program impact (activation, time-to-first-value, feature adoption), and feed recurring insights back to Support, Product, and Field Engineering to continuously improve the scaled journey.

Who You Are

• 3–5 years of relevant experience in a similar role
• Strong programming proficiency in Python
• Experience with deep learning frameworks (TensorFlow/Keras, PyTorch Lightning) and tools (e.g., Streamlit, LangChain)
• Familiarity with cloud platforms (AWS, GCP, Azure)
• Excellent communication and presentation skills, both written and verbal
• Organized and outcomes-driven: you can run programs, measure impact, and iterate.

Preferred

• Experience with GenAI and LLMs
• Proficiency with HuggingFace, Fastai, scikit-learn, XGBoost, LightGBM, or Ray
• Experience with hyperparameter optimization solutions
• Background in data engineering, MLOps, or LLMOps, with tools such as Docker and Kubernetes
• Familiarity with data pipeline tools

Wondering if you’re a good fit?
We believe in investing in our people, and value candidates who can bring their own diversified experiences to our teams – even if you aren't a 100% skill or experience match. Here are a few qualities we’ve found compatible with our team. If some of this describes you, we’d love to talk.

• You love collaborating with top AI teams to solve real-world ML and GenAI challenges
• You’re curious about how to optimize AI workflows at scale
• You’re an expert in Python and ML tooling, with a passion for enabling customer success

Why Us?
We work hard, have fun, and move fast! We’re in an exciting stage of hyper-growth that you will not want to miss out on. We’re not afraid of a little chaos, and we’re constantly learning. Our team cares deeply about how we build our product and how we work together, which is represented through our core values:

• Be Curious at Your Core
• Act Like an Owner
• Empower Employees
• Deliver Best-in-Class Client Experiences
• Achieve More Together

We support and encourage an entrepreneurial outlook and independent thinking. We foster an environment that encourages collaboration and provides the opportunity to develop innovative solutions to complex problems. As we get set for takeoff, the growth opportunities within the organization are constantly expanding. You will be surrounded by some of the best talent in the industry, who will want to learn from you, too. Come join us!

The base salary range for this role is $139,000 to $204,000. The starting salary will be determined based on job-related knowledge, skills, experience, and market location. We strive for both market alignment and internal equity when determining compensation. In addition to base salary, our total rewards package includes a discretionary bonus, equity awards, and a comprehensive benefits program (all based on eligibility).

About the Role:

As a Senior Data Scientist at Perpay, you will be a critical player in the day-to-day success of the business, supporting new and existing product initiatives through strong analytical skills and machine learning expertise. In addition, you will be responsible for helping shape the future of the data science team by developing processes and systems that scale with our rapidly growing business. This is an exciting opportunity to make a positive impact on the execution of Perpay’s mission and the lives of our customers. We are looking for someone who is passionate about all aspects of data science: from machine learning and exploratory data analysis to communication of results and real-time deployments. This crucial role will sit on our data science team and report to the Data Science Manager. If you’re looking for a collaborative team that you can bounce ideas off of and learn from, this is where you belong. We are about being curious, setting the standard and building good habits at Perpay.

Our greatest strength is our people and we’d love for you to be one of them!

Responsibilities:

• Drive the end-to-end completion of data science initiatives, collaborating with stakeholders from ideation through execution, ensuring projects deliver strong impact to our customer’s financial lives and the health of the business
• Proactively identify opportunities for the data science team to improve and expand Perpay’s business model using techniques from exploratory data analysis to machine learning models, leveraging subject matter expertise of Perpay’s business model
• Support the growth of the business by delivering scalable models and algorithms while maintaining and troubleshooting existing resources
• Deploy and maintain real-time model inference resources and infrastructure
• Deploy and maintain machine learning pipelines including batch processing and continuous training architecture
• Contribute to the cross-discipline adoption and responsible use of AI/LLM models and services at Perpay 
• Present results and analyses to the a wide range of audiences: from Perpay's leadership team to non-technical peers
• Contribute to defining standards and best practices concerning workflow and adoption of new technologies within the Data Science team
• Act as an informal mentor peers

What You’ll Bring:

• 5+ years of experience* and expertise applying machine learning techniques in industry
• 5+ years of experience* and expertise in producing data analysis, exploratory data analysis and data visualization in industry
• 5+ years of experience* and expertise in developing, deploying, maintaining real-time software (e.g. models exposed as APIs)
• 5+ years of experience* and expertise in Cloud compute infrastructure and best practices, including familiarity with tools such as terraform and Cloud Formation
• Bachelor's degree or higher in a quantitative/technical field (Mathematics, Statistics, Engineering, Chemistry, Physics)
• Excellent communication skills, across a range of audiences from technical to non-technical
• Strong business acumen, demonstrated through subject matter expertise in at least one industry
• Capable of managing projects “end to end” including managing project scope, timelines and workloads
• Advanced proficiency in Python and standard data science stack (e.g. scikit-learn, pandas, numpy, matplotlib, etc.)
• Team player: willingness and ability to establish strong working relationships not just within the data science team itself, but across the organization

*Internships not included

Nice to have:

• Expertise in multiple advanced machine learning methodologies: e.g. deep learning, probabilistic programming, mathematical optimization, etc.
• Deep familiarity in multiple advanced machine learning frameworks: e.g. tensorflow, pytorch, pymc, etc.
• Experience with scalable compute frameworks such as Spark or Polars
• Experience with deployment frameworks such as Kubernetes