As a Data Scientist at Lambda, your day-to-day work is directly tied to advancing the frontiers of generative AI. You will work hands-on across either the Fundamental Research or Applied Research tracks, depending on your expertise.

In the Fundamental track, you will focus on building efficient data pipelines, developing evaluation toolkits, and conducting research in foundation models for language, vision, or robotics. Your goal will be to push the state-of-the-art and publish your findings in top-tier ML conferences like NeurIPS, ICML, or CVPR.

In the Applied track, your responsibilities shift toward maximizing training and inference performance for large-scale AI systems. You will systematically evaluate models and agents, working closely with engineers to ensure that Lambda's compute resources are being utilized as efficiently as possible. Across both tracks, you will be expected to contribute to technical blogs, public datasets, and open-source machine learning projects, acting as an ambassador for Lambda's engineering culture.

Lambda looks for candidates who combine strong academic foundations with proven, hands-on engineering capabilities.

• Must-have skills – Proficiency in PyTorch or similar deep learning frameworks is non-negotiable. You must have demonstrated project experience or publications in relevant ML areas. Strong communication skills are also essential, as you will collaborate closely with world-class researchers and engineers.
• Educational background – You should be a BS, MS, or Ph.D. student (or recent graduate) in Computer Science or a related field, with a heavy focus on Machine Learning.
• Nice-to-have skills – Active contributions to open-source machine learning projects will make your application stand out. Experience with foundation models, multi-modal systems, or agentic frameworks is highly valued. Additionally, any background in dataset creation, system benchmarking, or optimizing model efficiency at scale is a major plus.

Q: What is the working arrangement for this role? This position requires a strong in-person presence. You are expected to be in the San Francisco office four days per week. Currently, Lambda's designated work-from-home day is Tuesday, fostering a highly collaborative, in-person research culture.

Q: How difficult is the technical coding challenge? The challenge is moderately difficult and highly practical. Rather than solving algorithmic puzzles (like LeetCode), you will be asked to complete a realistic ML task, such as fitting and optimizing a model on a standard dataset like COCO within a 2-hour window.

Q: Do I need top-tier conference publications to be hired? While having papers accepted at conferences like NeurIPS or CVPR is highly advantageous (especially for the Fundamental Research track), it is not strictly required. Demonstrated, high-quality project experience and open-source contributions can also prove your capabilities.

Q: What happens if I don't finish the technical challenge perfectly? The follow-up interview is just as important as the challenge itself. Interviewers care deeply about your thought process, how you prioritize tasks under time pressure, and your ability to articulate the trade-offs you made. A well-defended, partially complete solution often beats a completed but poorly understood one.

• Master your PyTorch fundamentals: You will not have time to look up basic syntax during the 2-hour challenge. Ensure you can write custom datasets, dataloaders, and training loops from memory.
• Prepare to defend your code: The final interview is a direct review of your technical challenge. Be prepared to explain every line of code, why you chose specific hyperparameters, and how you would improve the model with more time.
• Showcase hardware awareness: Lambda is a GPU company. Mentioning how your code optimizes GPU memory utilization or minimizes CPU-GPU transfer times will score you massive bonus points.

• Clarify your preferred track early: Be explicit about whether you lean toward Fundamental Research (modeling, multi-modal, toolkits) or Applied Research (systems, benchmarking, scaling). This helps interviewers tailor the conversation to your strengths.

Interviewing for a Data Scientist position at Lambda is an opportunity to showcase your ability to blend cutting-edge machine learning research with practical, systems-level execution. The company is actively building the infrastructure that powers the AI revolution, and they are looking for candidates who are passionate about making superintelligence accessible to everyone.

To succeed, focus heavily on your applied PyTorch skills, be ready to execute under a time limit during the technical challenge, and prepare to defend your architectural decisions with confidence. Remember that the initial rounds are just as crucial as the coding challenge; use them to build rapport, express your genuine interest in AI infrastructure, and align your research goals with Lambda's mission.

The compensation data above provides a baseline for the role, though actual offers will vary based on your specific experience level, academic background, and whether you are joining as an intern or full-time. Use this to understand the market positioning as you prepare for your interviews.

You have the technical foundation required to excel in this process. Approach the coding challenge like a real day on the job, communicate your thought process clearly, and lean into your unique research experiences. For more insights, practice scenarios, and community advice, continue exploring resources on Dataford. Good luck—you are ready for this!