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Machine Learning Engineer - Customer Enablement

In 2012, Lambda started with a crew of AI engineers publishing research at top machine-learning conferences. We began as an AI company built by AI engineers. That hasn't changed. Today, we're on a mission to be the world's top AI computing platform. We equip engineers with the tools to deploy AI that is fast, secure, affordable, and built to scale. Whether they need powerhouse GPU hardware on-site or the flexibility of cloud-based solutions, we've got the horsepower to make it happen. Lambda’s AI Cloud has been adopted by the world’s leading companies and research institutions including Anyscale, Rakuten, The AI Institute, and multiple enterprises with over a trillion dollars of market capitalization. Our goal is to make computation as effortless and ubiquitous as electricity.

*Note: This position requires presence in our San Francisco office location 4 days per week; Lambda’s designated work from home day is currently Tuesday.

What You’ll Do

  • Guide new customers through the technical onboarding process by:

    • Assisting ML researchers in migrating their existing workloads to Lambda’s AI Cloud Platform, ensuring that expected performance is achieved

    • Providing initial troubleshooting for technical issues that arise during the first few days of customers time on Lambda infrastructure

  • Collaborate closely with customers to understand their needs and objectives, offer tailored guidance and best practices for deploying models and managing GPU infrastructure

  • Demonstrate how to optimize and scale training and inference workloads within Lambda by:

    • Building proof-of-concept demos

    • Creating detailed architecture diagrams

  • Create and maintain detailed documentation including technical guides, best practices and troubleshooting resources

  • Conduct training sessions and workshops for customers, enabling them to effectively utilize Lambda’s products and services

  • Facilitate smooth workload transitions between Lambda’s various products 

  • Drive customer growth by identifying opportunities to increase product adoption

  • Act as a trusted advisor to new customers, ensuring successful integration and optimization of Lambda products

  • Provide continuous customer feedback to influence product roadmap and enhancements

  • Serve as a link between customers and internal teams

You 

  • Have experience in machine learning or data science with a deep understanding of model development, and deployment

  • Have experience using deep learning frameworks and libraries such as PyTorch, Tensorflow, Deepspeed, etc. 

  • Have experience with containerization technologies such as Docker and Kubernetes 

  • Have experience building and optimizing LLM-based applications

  • Have experience building end-end ML pipelines on major cloud platforms

  • Have experience with Linux systems administration

  • Are an excellent communicator, capable of explaining complex, technical concepts to technical and non-technical audiences

  • Are customer obsessed, and strive to deliver exceptional experiences to current and future Lambda customers

  • Experience as an ML educator and/or building and executing customer training sessions, product demos or workshops

Nice to Have

  • Experience using MLOps tools such as RunAI, Weights and Biases, ClearML

  • Experience in training large models using distributed systems

    • Selecting parallelism strategies

    • Multi-GPU and Multi-Node training

    • Troubleshooting and configuring NCCL/RDMA

    • Quantization

  • Experience with HPC orchestration technologies such as SLURM

  • Experience with automation tools like Ansible, Puppet, Salt

Salary Range Information 

Based on market data and other factors, the salary range for this position is $144,000 - $210,000. However, a salary higher or lower than this range may be appropriate for a candidate whose qualifications differ meaningfully from those listed in the job description.

About Lambda

  • We offer generous cash & equity compensation

  • Investors include US Innovative Technology, Gradient Ventures, Mercato Partners, SVB

  • We are experiencing extremely high demand for our systems, with quarter over quarter, year over year profitability

  • Our research papers have been accepted into top machine learning and graphics conferences, including NeurIPS, ICCV, SIGGRAPH, and TOG

  • We have a wildly talented team of 350+, and growing fast

  • Health, dental, and vision coverage for you and your dependents

  • Commuter/Work from home stipends for select roles

  • 401k Plan with 2% company match

  • Flexible Paid Time Off Plan that we all actually use

A Final Note:

You do not need to match all of the listed expectations to apply for this position. We are committed to building a team with a variety of backgrounds, experiences, and skills.

Equal Opportunity Employer

Lambda is an Equal Opportunity employer. Applicants are considered without regard to race, color, religion, creed, national origin, age, sex, gender, marital status, sexual orientation and identity, genetic information, veteran status, citizenship, or any other factors prohibited by local, state, or federal law.

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Average salary estimate

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$144000K
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What You Should Know About Machine Learning Engineer - Customer Enablement, Lambda

At Lambda, we're on the cutting edge of AI technology, and we're excited to announce an opening for a Machine Learning Engineer - Customer Enablement in our San Francisco office. Our mission is simple: to provide robust AI computing solutions that empower engineers to embrace the future of machine learning. As a Machine Learning Engineer on our team, you'll guide new customers through the onboarding process, helping to migrate their workloads onto our AI Cloud Platform with ease. Your expertise will ensure that they achieve their performance goals, while you tackle technical issues as they arise in those early days. You'll work closely with customers to provide tailored advice for deploying their models and managing GPU infrastructure, acting as their trusted advisor in this journey. We want you to share your knowledge and skills, which means creating valuable documentation and conducting engaging training sessions. Your role will extend beyond direct customer support; you'll analyze feedback to refine our offerings and drive product adoption. At Lambda, we value exceptional communication skills, as you'll be explaining complex technical concepts to both technical and non-technical audiences. If you have a passion for machine learning, a commitment to providing outstanding customer service, and a desire to work with like-minded professionals, we want to hear from you! Join us and be part of a world where computation feels as natural and available as electricity.

Frequently Asked Questions (FAQs) for Machine Learning Engineer - Customer Enablement Role at Lambda
What are the main responsibilities of a Machine Learning Engineer - Customer Enablement at Lambda?

As a Machine Learning Engineer - Customer Enablement at Lambda, your primary responsibilities include guiding new customers through the onboarding process, assisting them with migrating workloads to our AI Cloud Platform, providing initial troubleshooting support, and building proof-of-concept demos. You'll also create detailed architecture diagrams and maintain documentation to help customers utilize our products effectively.

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What qualifications are needed to become a Machine Learning Engineer - Customer Enablement at Lambda?

To qualify for the Machine Learning Engineer - Customer Enablement role at Lambda, candidates should have experience in machine learning or data science, a deep understanding of model development and deployment processes, as well as familiarity with deep learning frameworks like PyTorch or TensorFlow. Additionally, experience with containerization technologies like Docker, knowledge of building ML pipelines on cloud platforms, and excellent communication skills are essential.

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What does the onboarding process for customers look like at Lambda from the perspective of a Machine Learning Engineer?

In your role as a Machine Learning Engineer - Customer Enablement at Lambda, you will guide customers through a comprehensive onboarding process that includes assisting with workload migrations, troubleshooting technical issues, and providing tailored best practices. You will help them optimize their GPU infrastructure and ensure they have the tools and knowledge necessary to leverage our AI Cloud effectively.

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What kind of training will I provide as a Machine Learning Engineer - Customer Enablement at Lambda?

As a Machine Learning Engineer - Customer Enablement at Lambda, you will conduct training sessions and workshops that empower customers to effectively use our products and services. This may include developing training materials, sharing technical knowledge, and demonstrating the optimization of AI workloads across various applications.

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Is experience with MLOps tools necessary for the Machine Learning Engineer - Customer Enablement position at Lambda?

While not mandatory, having experience with MLOps tools such as Weights & Biases or RunAI is beneficial for the Machine Learning Engineer - Customer Enablement role at Lambda. This experience can enhance your ability to assist customers in managing their machine learning workflows more effectively.

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Common Interview Questions for Machine Learning Engineer - Customer Enablement
Can you explain your experience with machine learning frameworks and how they relate to this role?

When answering this question, highlight your experience with deep learning frameworks like PyTorch or TensorFlow, focusing on specific projects where you used these tools. Discuss how familiarity with these frameworks enables you to assist customers in optimizing their models within Lambda's platform.

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What strategies do you use to troubleshoot technical issues during customer onboarding?

In your response, describe your systematic approach to troubleshooting, such as actively listening to the customer’s problems, collecting relevant logs and data, and collaborating with internal teams if necessary. Emphasize your commitment to maintaining a positive customer experience even during challenges.

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How would you tailor your communication style when explaining complex technical concepts to different audiences?

Discuss your ability to assess the technical background of your audience and adjust your explanations accordingly. For technical audiences, you may use more jargon and in-depth discussions, while for less technical stakeholders, you'll employ analogies or simpler terms to ensure understanding.

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What is your experience with running trainings or workshops in a technical environment?

Share examples of previous training sessions you facilitated, focusing on the structure, tools used, and feedback received. Highlight your ability to engage participants and ensure they leave with actionable knowledge.

Join Rise to see the full answer
How do you keep up with industry trends in machine learning and AI?

You can mention following thought leaders on social media, subscribing to industry newsletters, attending conferences, and participating in discussions in forums like Stack Overflow or GitHub. Show your commitment to lifelong learning in the AI field.

Join Rise to see the full answer
Describe a challenging situation with a customer and how you managed it.

Reflect on a specific scenario where you faced a difficult issue, explain the context and your thought process in handling the situation. Emphasize your communication skills and dedication to finding a resolution that prioritized the customer’s needs.

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What steps would you take to successfully migrate a customer workload to Lambda's AI Cloud?

Outline a clear plan that includes initial consultation with the customer to assess their needs, preparing the data and models for migration, executing the move, and conducting post-migration support to ensure everything runs smoothly.

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How do you identify customer needs for product adoption?

Discuss your experience with gathering customer feedback, analyzing usage patterns, and conducting assessments during onboarding to determine areas where enhancements can boost product utilization and satisfaction.

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What do you think makes a successful Machine Learning Engineer at Lambda?

Focus on the importance of technical expertise in machine learning, strong customer service skills, excellent communication, and the ability to work collaboratively within a fast-paced AI environment. Discuss how these traits contribute to the success of both the individual and Lambda’s mission.

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How do you document processes and best practices for technical guidance?

Explain your approach to documenting processes—this could involve creating clear step-by-step guides, using visuals like flowcharts and diagrams, and keeping documentation up to date. Highlight the importance of accessible resources for customer empowerment.

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Lambda provides Artificial Intelligence and Machine Learning infrastructure to companies like Apple, Intel, Microsoft, MIT, Harvard, the Federal Government, and the DOD. Were headquartered in the Dogpatch and are a short walk from the 22nd Street ...

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Full-time, on-site
DATE POSTED
December 22, 2024

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