How to Apply to Poolside AI

10 min read Last updated March 7, 2026 9 open positions
Blake Crosley
Blake Crosley Researched using publicly available information, hiring data, and ATS analysis.

Key Takeaways

  • Tailor your resume to the specific parenthetical specialization in the role title — Poolside's 'Member of Engineering' roles are not interchangeable, and your application should prove depth in exactly the domain listed.
  • Format your resume as a single-column PDF with standard headings and no graphics to ensure Ashby parses it cleanly and Poolside's recruiters can find you via keyword searches.
  • Quantify your work with the metrics that matter in AI infrastructure: model parameter counts, GPU cluster sizes, training throughput, latency benchmarks, and data volumes.
  • Research Poolside's specific mission — AI purpose-built for software engineering — and be prepared to articulate why this focus area excites you and how your experience connects to it.
  • Complete every optional field in Ashby's application form, especially GitHub and publication links, as these give Poolside's technical reviewers immediate evidence of your capabilities.
  • Prepare for interviews by practicing system design scenarios related to large-scale model training, inference serving, or agent sandbox architectures — Poolside's problems are real-world infrastructure challenges, not textbook exercises.
  • If you hold an active security clearance, highlight it prominently on your resume to fast-track consideration for Poolside's forward-deployed and government-adjacent roles.

About Poolside AI

Poolside AI is a frontier AI company building foundation models purpose-built for software engineering and code generation. Unlike general-purpose large language model companies, Poolside focuses exclusively on creating the world's most capable coding AI — training models from scratch on code-specific data to achieve superior performance on real-world software development tasks. The company has attracted substantial venture capital backing and assembled a team of researchers and engineers from top AI labs, positioning itself as a serious contender in the rapidly expanding AI-assisted development space. Poolside's culture reflects the intensity and ambition of a well-resourced AI startup operating at the frontier. Their distinctive use of "Member of Engineering" as a universal title signals a deliberately flat organizational structure where individual contribution matters far more than hierarchy. With roles spanning pre-training, inference, CUDA optimization, agent sandboxes, and forward-deployed research engineering, the company operates across the full stack of AI model development and deployment. The inclusion of clearance-required roles also suggests partnerships with government and defense sectors. People are drawn to Poolside because it offers the rare opportunity to work on foundational AI infrastructure with direct, tangible impact on how software gets built. The team is small enough that every engineer's work visibly moves the product forward, yet well-funded enough to access the compute resources and talent density typically reserved for much larger organizations. For engineers passionate about low-level systems optimization, large-scale model training, and the future of AI-augmented coding, Poolside represents one of the most compelling opportunities in the current AI landscape.

Application Process

  1. 1
    Identify Your Best-Fit Role on Poolside's Careers Page

    Visit poolside.ai/careers and carefully review the 9+ open openings. Pay close attention to the parenthetical specializations — roles like "Member of Engineering (Pre-training / CUDA)" versus "Member of Engineering (Scalability)" target very different skill sets despite sharing the same base title. Match your deepest technical expertise to the specific domain listed, as Poolside's flat title structure means the specialization descriptor is what actually defines the role.

  2. 2
    Submit Your Application Through Ashby

    Poolside uses Ashby as its applicant tracking system, which powers a clean, modern application portal. You'll typically upload your resume, provide contact information, and answer any role-specific questions. Some roles may include short-answer or technical screening questions embedded directly in the application form — treat these with the same rigor you'd give a written interview response.

  3. 3
    Initial Application Review by Recruiting and Hiring Managers

    Given Poolside's lean team size and highly specialized roles, applications are commonly reviewed by both recruiters and the technical hiring manager. Expect scrutiny on your specific domain expertise — if you're applying for a CUDA role, they'll look for concrete evidence of GPU programming experience, not just general ML engineering. This stage may take one to three weeks depending on hiring velocity and the role's urgency.

  4. 4
    Recruiter or Hiring Manager Screen

    Many frontier AI startups begin with a 30-45 minute introductory call to assess baseline technical alignment and mutual interest. At Poolside, this conversation likely explores your understanding of large-scale model training, your familiarity with their problem space (AI for code), and your motivation for joining an early-stage, high-intensity environment. Be prepared to discuss specific technical projects in depth, not just at a surface level.

  5. 5
    Technical Assessment or Deep-Dive Interview

    For engineering roles, expect a rigorous technical evaluation tailored to the role's specialization. Pre-training roles may involve questions about distributed training, data pipelines, and optimization techniques. Inference roles may focus on latency optimization, quantization, or serving infrastructure. Some roles may include a take-home exercise or live coding session focused on systems-level programming in Python, C++, or CUDA.

  6. 6
    On-Site or Virtual Interview Loop

    The full interview loop at a company of Poolside's profile typically involves 3-5 sessions over a half or full day. Expect a mix of deep technical discussions, system design conversations, and culture-fit assessments. Given the "Member of Engineering" flat hierarchy, interviewers likely evaluate your ability to operate with high autonomy, make sound technical decisions independently, and collaborate across research and engineering boundaries.

  7. 7
    Offer and Negotiation

    Poolside, as a well-funded AI startup, typically offers competitive compensation packages that may include meaningful equity components. The offer stage often moves quickly at companies in this space, especially for candidates with competing offers from other AI labs. If you're applying for a clearance-required role (such as the Founding Forward Deployed Research Engineer), factor in additional time for security clearance processing.

Resume Tips for Poolside AI

critical

Lead with Your Specific Technical Domain Expertise

Poolside's roles are narrowly specialized — a "Pre-training / CUDA" role is fundamentally different from a "Data Platform Lead" role. Your resume's top third should immediately signal your depth in the exact domain you're targeting. If applying for a CUDA role, your summary or first experience bullet should reference GPU kernel optimization, CUDA programming, or distributed training on GPU clusters. Don't bury this expertise in a skills list at the bottom.

critical

Quantify Scale: Parameters, Tokens, Cluster Size, Latency

AI infrastructure work is defined by scale. Include specific metrics: the size of models you've trained (parameter count), the volume of data you've processed (token count or terabytes), the compute infrastructure you've managed (number of GPUs, cluster configurations), and the performance improvements you've achieved (latency reductions, throughput gains). Poolside is building frontier models, so evidence that you've operated at meaningful scale is a differentiator.

critical

Use Ashby-Compatible Formatting for Clean Parsing

Ashby's resume parser handles standard formats well but can struggle with multi-column layouts, text boxes, headers/footers, and heavily designed templates. Use a single-column layout with clear section headings (Experience, Education, Skills, Publications). Save as PDF with selectable text — not a scanned image. Avoid icons, graphics, or infographic-style skill bars that Ashby cannot extract as text data.

recommended

Mirror Poolside's Technical Vocabulary in Your Resume

Study Poolside's job descriptions for the exact terminology they use: "agent sandboxes," "pre-training," "inference," "scalability," "forward deployed." These terms likely appear in Ashby's keyword matching and in manual reviewer scans. If you have experience with reinforcement learning from human feedback (RLHF), code generation models, or training data curation for code, use those exact phrases rather than generic equivalents like "machine learning pipeline development."

recommended

Highlight Open-Source Contributions and Research Publications

Frontier AI companies place significant weight on visible, verifiable technical output. Include links to your GitHub profile (especially repos related to model training, inference optimization, or developer tools), published papers (particularly in NeurIPS, ICML, ACL, or relevant workshops), and any contributions to open-source ML frameworks like PyTorch, vLLM, or Triton. These serve as concrete proof of your technical capability beyond what resume bullets alone can convey.

recommended

Demonstrate Autonomy and Ownership in Past Roles

Poolside's flat "Member of Engineering" title structure implies they value engineers who drive projects from conception to completion without heavy management oversight. Frame your experience bullets around outcomes you owned end-to-end, not tasks you were assigned. "Designed and implemented a distributed training pipeline that reduced pre-training time by 40%" is far stronger than "Contributed to the training infrastructure team's efforts to improve efficiency."

nice_to_have

Keep It Concise — Two Pages Maximum for Most Candidates

At a startup building at Poolside's pace, hiring managers reviewing your application in Ashby are time-constrained. For candidates with fewer than 15 years of experience, a tightly edited two-page resume is ideal. Prioritize the last 5-7 years of experience and cut older roles to single lines unless they're directly relevant. Research engineers with extensive publication records can include a selected publications section but should still keep the core resume compact.

nice_to_have

Include Relevant Security Clearance Status if Applicable

Poolside's Forward Deployed Research Engineer role explicitly requires clearance. If you hold an active TS/SCI, Top Secret, or Secret clearance, state this prominently near the top of your resume. If your clearance is inactive but reinstateable, note that as well. This information can accelerate your candidacy for clearance-required positions and is a meaningful differentiator that Poolside's recruiting team will screen for early in the process.

ATS System: Ashby

Ashby is a modern applicant tracking system popular among well-funded startups and fast-growing tech companies. It combines ATS functionality with recruiting analytics and CRM capabilities, offering a streamlined candidate experience. Ashby's parsing engine extracts structured data from resumes and uses it for candidate search, filtering, and pipeline management by recruiting teams.

  • Use a single-column PDF format with selectable text to ensure Ashby's parser accurately extracts your experience, education, and skills sections.
  • Include exact keywords from Poolside's job descriptions — Ashby allows recruiters to search and filter candidates by specific terms like 'CUDA,' 'pre-training,' 'distributed systems,' or 'inference optimization.'
  • Avoid placing critical information in headers, footers, or text boxes, as Ashby's parser may skip these regions during extraction.
  • Use standard section headings ('Experience,' 'Education,' 'Skills,' 'Publications') so Ashby correctly categorizes your resume content into its structured fields.
  • Fill out all application form fields completely — Ashby's candidate profiles are built from both your resume and your form responses, and incomplete fields may lower your visibility in filtered searches.
  • If Ashby presents optional questions or fields for links (GitHub, portfolio, LinkedIn), always complete them — Poolside's hiring team likely uses these to quickly assess technical depth before opening your full resume.
  • Keep file names professional and simple (e.g., 'FirstName_LastName_Resume.pdf') as the filename is visible to reviewers within Ashby's interface.

Complete Ashby Resume Guide →

Interview Culture

Poolside AI's interview process reflects the intensity, technical rigor, and collaborative ethos of a frontier AI research company operating at startup speed.

Based on patterns common among well-funded AI startups of this profile, candidates should expect a multi-stage process that prioritizes deep technical expertise, systems thinking, and the ability to operate with significant autonomy. The process typically begins with a recruiter screen focused on role alignment, motivation, and logistical fit (location, clearance if applicable, start date). This is followed by a technical screen — often conducted by a senior engineer or the hiring manager — that dives into your specific domain. For pre-training roles, expect discussions about training stability, data pipeline design, and scaling laws. For inference roles, prepare for questions about serving architectures, latency optimization, and quantization tradeoffs. CUDA-specific roles will likely include questions about kernel optimization, memory management, and GPU architecture. The full interview loop commonly spans 4-5 sessions and may include a system design round where you architect a solution to a real problem Poolside faces — think designing a distributed training pipeline or an agent execution sandbox. Live coding may focus on Python or C++ depending on the role, with an emphasis on correctness, performance awareness, and clean systems thinking rather than algorithmic puzzle-solving. Culture fit at Poolside is not about personality tests — it's about working style. Interviewers are likely assessing whether you thrive in ambiguity, can make decisions without extensive process or approval chains, and whether you communicate technical ideas clearly to cross-functional collaborators. The flat "Member of Engineering" structure means there are fewer layers between your work and the company's outcomes, so interviewers want to see evidence that you've owned meaningful technical decisions and shipped results. Prepare to discuss not just what you built, but why you made specific technical tradeoffs. Poolside's interviewers are researchers and engineers building at the frontier — they respect candor about what didn't work as much as they value accomplishments. Showing intellectual curiosity about Poolside's specific challenges in code generation AI will also signal genuine interest beyond generic enthusiasm for the AI space.

What Poolside AI Looks For

  • Deep systems-level expertise in at least one core domain: distributed training, CUDA/GPU programming, inference optimization, or large-scale data infrastructure
  • Demonstrated ability to operate autonomously and drive technical projects from design through production without heavy management direction
  • Experience working at meaningful scale — training large models, managing GPU clusters, or processing terabyte-scale datasets
  • Strong fundamentals in Python and/or C++ with an emphasis on performance-critical code, not just scripting
  • Intellectual curiosity about AI for code generation specifically — understanding of how LLMs can be applied to software engineering workflows, agent-based systems, and developer tooling
  • Clear, precise technical communication — the ability to articulate complex system design decisions and tradeoffs to both research and engineering audiences
  • Comfort with the pace, ambiguity, and ownership expectations of an early-stage, well-funded AI startup where priorities can shift quickly
  • For clearance-required roles: active or reinstateable U.S. security clearance and willingness to work in classified environments

Frequently Asked Questions

How long does the Poolside AI application review process typically take?
Based on patterns at similarly sized, well-funded AI startups, initial application review typically takes one to three weeks. Poolside's team is lean and their roles are highly specialized, which means hiring managers likely review applications personally rather than delegating entirely to recruiters. If your background closely matches a role's specific requirements, you may hear back faster. If you haven't received a response after three weeks, a polite follow-up email to the recruiting team is appropriate.
Does Poolside AI require a cover letter with applications?
Poolside's Ashby-powered application portal may or may not include an optional cover letter field depending on the role. Even if it's optional, submitting a concise, targeted cover letter can differentiate you — especially given how specialized these roles are. Use the cover letter to explain why you're specifically drawn to AI for code generation (not just 'AI' broadly), connect your background to the exact role specialization, and demonstrate that you understand Poolside's mission. Keep it under 300 words and lead with your most relevant technical credential.
What experience level does Poolside AI hire for?
Poolside's job titles use 'Member of Engineering' and 'Founding' prefixes rather than junior/senior/staff designations, suggesting they prioritize capability over years of experience. That said, the roles listed — involving CUDA optimization, distributed pre-training, and inference infrastructure — require deep domain expertise that typically comes with several years of hands-on experience. The 'Founding' Forward Deployed Research Engineer title indicates a role with significant ownership and influence, pointing toward senior or staff-level expectations. Entry-level candidates may find limited opportunities unless they bring exceptional research publications or open-source contributions in directly relevant areas.
What programming languages and technical skills should I highlight for Poolside AI?
Python is central — Poolside has a dedicated 'Member of Engineering (Python)' role, and Python is the lingua franca of ML engineering. For pre-training and inference roles, CUDA and C++ experience is highly valued, as these roles involve GPU kernel optimization and low-level performance work. Beyond languages, highlight experience with distributed training frameworks (PyTorch, DeepSpeed, Megatron-LM), inference serving systems (vLLM, TensorRT, Triton Inference Server), and data pipeline tools. Familiarity with large-scale compute orchestration on cloud infrastructure (AWS, GCP) and containerized deployment is also relevant across most roles.
Does Poolside AI offer remote work options?
Poolside's careers page and individual job listings are the best source for current remote work policies. Many frontier AI startups of Poolside's profile offer hybrid or remote-flexible arrangements for engineering roles, though some positions — particularly clearance-required roles like the Forward Deployed Research Engineer — may have strict on-site or location requirements due to security constraints. Check each role's listing carefully for location specifications, and raise this question early in the recruiter screen if the listing is ambiguous.
How should I prepare for a technical interview at Poolside AI?
Preparation should be laser-focused on the specific domain of the role you're applying for. For pre-training roles, review distributed training concepts (data parallelism, tensor parallelism, pipeline parallelism), training stability techniques, and data processing at scale. For inference roles, study model serving architectures, batching strategies, quantization methods, and latency optimization. For CUDA roles, practice writing and optimizing GPU kernels, understanding memory hierarchies, and profiling performance bottlenecks. Beyond technical preparation, familiarize yourself with Poolside's product space — read about current approaches to AI-assisted code generation, agent-based development tools, and the challenges of training code-specific models. Being able to discuss how your work connects to these challenges signals genuine engagement.
What is the 'Member of Engineering' title, and what does it mean about Poolside's culture?
Poolside's use of 'Member of Engineering' as a universal title across roles — from CUDA specialists to data platform leads — is a deliberate cultural signal. It reflects a flat organizational structure where hierarchy is minimized and technical contribution is valued over title inflation. This pattern is common among elite, research-oriented AI organizations that want to attract top talent by emphasizing impact over career ladder progression. For applicants, this means you should expect an environment with high autonomy, minimal bureaucracy, and the expectation that every team member drives meaningful outcomes. Frame your application and interview responses accordingly — emphasize ownership and initiative over team size managed or title progression.
Should I apply to multiple roles at Poolside AI simultaneously?
Applying to one, or at most two closely related roles, is the strongest approach. Ashby allows Poolside's recruiters to see all roles you've applied to, and submitting applications to five different specializations signals that you lack clear domain focus — which conflicts with the deep expertise these roles require. If you genuinely have strong qualifications for two related roles (e.g., Pre-training / CUDA and Inference), apply to the one where your experience is strongest and mention in your cover letter or application notes that you'd also be interested in the adjacent role. This shows focus while keeping options open.
What makes Poolside AI different from other AI companies I could apply to?
Poolside's core differentiator is its exclusive focus on building foundation models for software engineering rather than general-purpose AI. While companies like OpenAI, Anthropic, and Google DeepMind build broad models, Poolside is betting that code-specific models trained on code-specific data will dramatically outperform generalist models at software development tasks. This focus creates a unique working environment: you're not building a chatbot or an image generator — you're building AI that writes, understands, and reasons about software. For engineers who are passionate about both AI infrastructure and developer tooling, this vertical focus means your work has a clearer, more direct connection to the end product. The company's substantial funding also means access to significant compute resources — a practical consideration for anyone whose work depends on training large models.

Sample Open Positions

Check Your Resume Before Applying → View 9 open positions at Poolside AI

Related Resources

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Sources

  1. Poolside AI Careers Page — Poolside AI
  2. Poolside AI Official Website — Poolside AI
  3. Ashby ATS — How It Works for Candidates — Ashby
  4. Poolside AI Company Profile and Reviews — Glassdoor