How to Apply to Baseten

Key Takeaways

Study Baseten's Truss framework on GitHub before applying — being able to reference their actual technology in your application and interviews demonstrates genuine interest that generic AI enthusiasm cannot match
Tailor your resume to emphasize infrastructure, systems, and ML deployment experience using Baseten's exact terminology from their job descriptions — 'model inference,' 'GPU orchestration,' and 'cold start optimization' should appear naturally in your bullet points
Submit your application as a clean, single-column PDF through Ashby, completing every field including optional ones, and include your GitHub and LinkedIn URLs in dedicated profile fields
Prepare for systems design interviews by thinking through real Baseten-relevant scenarios: GPU cluster autoscaling, model caching strategies, latency optimization for inference APIs, and the tradeoffs of different model serving architectures
Research Baseten's competitive landscape (Replicate, Modal, AWS SageMaker, Together AI) so you can speak intelligently about market positioning during recruiter screens and culture conversations
Demonstrate startup readiness by highlighting specific examples of building from scratch, operating with ambiguity, wearing multiple hats, and shipping quickly — these traits are weighted heavily at early-to-mid-stage companies
Follow up promptly after each interview stage and keep communications concise and professional — Baseten's hiring process typically moves fast, and responsiveness signals the urgency and engagement they value

About Baseten

Baseten is a high-growth AI infrastructure company building the platform layer that powers machine learning model inference at scale. Their core product enables engineering teams to deploy, manage, and scale ML models in production with minimal friction — a critical capability as enterprises race to integrate AI into their products. Baseten is known for Truss, their open-source framework for serving ML models, and their managed inference platform that handles GPU orchestration, autoscaling, and performance optimization. Positioned at the intersection of cloud infrastructure and applied AI, Baseten competes in the rapidly expanding MLOps and model serving market alongside players like Replicate, Modal, and AWS SageMaker, but differentiates through developer experience and performance-first engineering. The company has raised significant venture capital funding and counts high-profile AI companies among its customers, giving it strong momentum in the generative AI wave. Culturally, Baseten operates with the intensity and speed characteristic of a well-funded Series B startup — small teams with outsized ownership, rapid iteration cycles, and a deep technical bench. Engineers here aren't just building CRUD apps; they're solving GPU scheduling, kernel optimization, and low-latency inference challenges that sit at the bleeding edge of systems engineering. The team skews heavily technical, with a strong bias toward builders who thrive in ambiguity and want direct impact on the AI infrastructure stack. For candidates who want to work on problems that matter to the future of AI deployment — and want to do it alongside a concentrated group of exceptional engineers — Baseten represents one of the most compelling opportunities in the current startup landscape.

Application Process

Identify Your Target Role on Baseten's Careers Page
Visit baseten.co/careers to browse their 43+ open positions across engineering, sales, marketing, and enablement. Pay close attention to role distinctions — Baseten differentiates between specializations like 'Model Performance Tooling,' 'Internal Platform,' 'Model APIs,' and 'GPU Kernel Engineering,' each requiring distinct technical depth. Read full job descriptions carefully, as Baseten tends to be precise about required experience, particularly around ML infrastructure, distributed systems, and GPU-level programming.
Submit Your Application Through Ashby
Baseten uses Ashby as their applicant tracking system, which means your application flows through a modern, structured pipeline. Upload your resume (PDF strongly preferred for parsing reliability), complete any role-specific fields, and include a brief note about why Baseten's mission resonates with you. Ashby supports rich candidate profiles, so ensure your LinkedIn URL and any relevant GitHub or portfolio links are included in the appropriate fields.
Recruiter Screen and Initial Evaluation
If your profile aligns with the role's requirements, expect a 30-minute introductory call with a recruiter — likely someone from their growing talent team (they're actively hiring a Senior Sales Recruiter, indicating investment in scaling hiring operations). This conversation will typically cover your background, motivations for joining an AI infrastructure startup, compensation expectations, and timeline. Come prepared to articulate why you're drawn to the model inference and deployment space specifically, not just 'AI' broadly.
Technical or Functional Deep-Dive Interview
For engineering roles, expect a rigorous technical assessment — this could be a live coding session focused on systems design, a take-home project involving infrastructure or model serving concepts, or both. Baseten's engineering challenges typically involve real-world scenarios: designing autoscaling logic, optimizing inference latency, or building robust API layers. For non-engineering roles like Account Executive or Program Manager, anticipate a case study or role-play exercise that tests domain knowledge in AI/ML sales cycles or cross-functional program delivery.
Systems Design or Architecture Round
Engineering candidates, particularly at the senior level and above, will likely face a systems design round that probes your ability to think about distributed infrastructure at scale. Given Baseten's product — a GPU inference platform — expect questions touching on load balancing across GPU clusters, container orchestration, cold start optimization, or model caching strategies. Demonstrating familiarity with their open-source Truss framework or the broader model serving ecosystem (TensorRT, vLLM, Triton) will set you apart significantly.
Team and Culture Fit Conversations
Baseten typically includes a round of conversations with potential teammates and cross-functional partners. As a startup with concentrated teams, culture alignment matters enormously — they're looking for people who take ownership, communicate directly, and are comfortable with the pace of a company growing rapidly in a competitive market. Expect questions about how you handle ambiguity, how you prioritize when everything feels urgent, and what you've built from scratch versus inherited.
Final Decision, Reference Checks, and Offer
Baseten's hiring process typically moves quickly by startup standards — the entire loop may complete within two to three weeks for strong candidates. Reference checks are common for senior and leadership roles. Offers from venture-backed startups at Baseten's stage typically include a base salary, equity (stock options), and benefits. Be prepared to discuss your equity expectations and understand the vesting schedule, as this is a significant component of startup compensation.

Resume Tips for Baseten

Critical Lead with Infrastructure and ML Systems Experience

Baseten builds the infrastructure layer for AI model deployment — your resume should immediately signal relevant systems experience. Lead your bullet points with work involving distributed systems, GPU computing, Kubernetes, containerization, or ML model serving. If you've worked with inference optimization, autoscaling, or low-latency API design, these should appear in your top three bullet points for each relevant role, not buried at the bottom.

Critical Use Baseten's Domain Vocabulary Precisely

Ashby's parsing and recruiter screening will both benefit from alignment with Baseten's terminology. Mirror the language from their job postings: 'model inference,' 'GPU orchestration,' 'model serving,' 'cold start optimization,' 'kernel engineering,' 'forward-deployed engineering,' and 'LLM deployment.' Avoid generic terms like 'machine learning projects' when you can be specific — 'reduced p99 inference latency by 40% for a transformer-based NLP model' is far more compelling and keyword-aligned.

Critical Quantify Performance and Scale Metrics

Baseten operates at the infrastructure layer where latency, throughput, uptime, and cost efficiency are the metrics that matter. Wherever possible, include specific numbers: requests per second, latency percentiles, GPU utilization rates, cost savings from optimization, number of models deployed, or cluster sizes managed. A bullet point like 'Optimized GPU memory allocation across a 200-node cluster, reducing inference costs by 35%' immediately signals the scale and relevance of your experience.

Highlight Open-Source Contributions and Developer Tools Work

Baseten maintains Truss as an open-source project and operates in a deeply developer-facing market. If you've contributed to open-source ML frameworks, built developer tools, or created libraries that other engineers use, feature this prominently. Include GitHub links directly in your resume header. Contributions to projects like vLLM, Triton, PyTorch, or ONNX Runtime are particularly relevant and demonstrate community engagement that Baseten values.

Demonstrate Startup Velocity and Ownership

As a startup, Baseten values candidates who've operated in fast-paced, resource-constrained environments. Use language that conveys ownership: 'designed and shipped,' 'led from concept to production,' 'sole engineer responsible for,' or 'built from zero to launch in X weeks.' If you've worn multiple hats — say, an engineer who also did customer-facing work or shaped product direction — highlight that versatility explicitly, as Baseten's Forward Deployed Engineering roles specifically seek this blend.

Format for Ashby's Parsing Engine

Ashby handles modern resume formats well, but you still want to maximize parsing accuracy. Use a clean, single-column layout with standard section headers (Experience, Education, Skills). Submit as PDF to preserve formatting. Avoid tables, multi-column layouts, headers/footers with critical information, and images. Ashby extracts structured data effectively from well-organized resumes, so standard reverse-chronological format with clear company names, titles, dates, and locations works best.

Include a Targeted Skills Section with Technical Specifics

Create a dedicated skills section that maps to Baseten's technical stack. Include specific technologies: Python, Go, Rust, CUDA, TensorRT, Kubernetes, Docker, Terraform, AWS/GCP GPU instances, PyTorch, gRPC, and any model serving frameworks. For non-engineering roles, include relevant tools and domain knowledge — sales candidates should list CRM experience, AI/ML market knowledge, and enterprise sales methodologies relevant to infrastructure deals.

Keep It Concise and High-Signal

Startup hiring teams review resumes quickly. Aim for one page if you have under 8 years of experience, two pages maximum for senior and leadership candidates. Every bullet point should pass the 'so what?' test — if it doesn't demonstrate relevant skill, measurable impact, or unique qualification for Baseten's mission, cut it. Prioritize depth in your most relevant 2-3 roles over breadth across every position you've ever held.

ATS System: Ashby

Ashby is a modern, all-in-one recruiting platform favored by high-growth startups like Baseten for its powerful analytics, structured hiring workflows, and superior candidate experience. It combines ATS, CRM, and scheduling capabilities, meaning your application data is used not just for tracking but for pipeline analytics — making completeness and accuracy of your profile particularly important.

Submit your resume as a PDF to ensure Ashby's parser preserves your formatting and extracts text accurately
Use standard section headers (Experience, Education, Skills, Projects) — Ashby's parser maps these reliably and uses them to structure your candidate profile
Include your LinkedIn URL and GitHub profile in dedicated fields rather than embedding them in your resume body, as Ashby uses these for candidate enrichment
Mirror exact phrases from the job description in your resume — Ashby supports keyword matching and hiring managers can filter candidates by specific skills and terms
Avoid tables, columns, text boxes, and graphics that may confuse the parser — Ashby handles clean layouts well but complex formatting can result in garbled data extraction
Complete all optional fields in the application form, including any short-answer questions — Ashby's structured data approach means partially completed applications may be deprioritized by hiring teams reviewing filtered pipelines
If you're applying to multiple Baseten roles, customize your resume for each — Ashby tracks applications per role and recruiters can see your full application history across positions

Complete Ashby Resume Guide

Interview Culture

Baseten's interview process reflects the company's engineering-first culture and the technical depth required to build AI infrastructure at scale. Expect a process that is thorough but respectful of your time — startups at Baseten's stage typically run a tight loop of 4-5 interviews completed within two to three weeks. For engineering roles, technical rigor is the centerpiece. You'll likely encounter a coding assessment focused on systems-level programming (think Python, Go, or Rust depending on the role), followed by a deep systems design session. Given that Baseten's product involves GPU orchestration, model serving, and low-latency API infrastructure, design questions will likely probe your ability to reason about distributed systems under real-world constraints: How would you design an autoscaling system for GPU inference? How do you handle cold starts for large language models? What tradeoffs exist between batching strategies for different model architectures? For specialized roles like GPU Kernel Engineer, expect questions that go deep into CUDA, memory optimization, and compute throughput. For go-to-market roles like Account Executive or Program Manager, the interview process emphasizes domain expertise in the AI/ML ecosystem and the ability to navigate complex enterprise sales cycles or cross-functional programs. Expect scenario-based questions: How would you sell infrastructure to a VP of Engineering evaluating build-versus-buy for model serving? How would you manage a marketing program launch across engineering, product, and sales stakeholders? Culture conversations at Baseten probe for traits essential to startup success: intellectual curiosity about the AI space, comfort with ambiguity, high agency, and a builder mentality. They tend to favor candidates who have strong opinions, loosely held — people who can defend a technical or strategic position but update their thinking when presented with new information. Interviewers are often senior engineers or leaders who are deeply embedded in the product, so come prepared to talk about Baseten's actual technology, their open-source Truss project, and the competitive landscape of model inference platforms. Demonstrating that you've done your homework on the company — not just the role — signals the kind of genuine interest that resonates with startup hiring teams.

What Baseten Looks For

Deep systems engineering expertise — experience with distributed systems, GPU computing, containerization, and infrastructure at scale is foundational for most engineering roles
AI/ML domain fluency — understanding the model lifecycle from training to deployment, familiarity with inference optimization, and awareness of the rapidly evolving LLM ecosystem
Builder mentality and high agency — a track record of taking projects from zero to one, making decisions with incomplete information, and shipping fast without sacrificing quality
Open-source engagement and developer community credibility — contributions to relevant ML or infrastructure projects, active GitHub profiles, and a genuine interest in developer tooling
Startup adaptability — comfort operating across functions, willingness to do work outside your job description, and the resilience to thrive in a fast-moving, resource-constrained environment
Customer empathy and forward-deployed thinking — particularly for engineering and enablement roles, the ability to understand customer pain points and translate them into technical solutions
Strong written and verbal communication — Baseten's remote and distributed nature means clear async communication and the ability to articulate complex technical concepts to diverse audiences is essential
Intellectual curiosity about the AI infrastructure space — genuine enthusiasm for the problems Baseten solves, not just 'AI' as a buzzword, but the actual infrastructure challenges of serving models at scale

Frequently Asked Questions

How long does Baseten's hiring process typically take from application to offer?

Based on patterns common at startups of Baseten's size and stage, the full interview loop typically completes within two to three weeks for candidates who progress through all stages. The initial application review may take 5-10 business days depending on volume, with the recruiter screen following shortly after. Engineering roles may include a take-home or live coding assessment that adds a few days to the timeline. Baseten appears to prioritize speed in hiring — as a competitive AI infrastructure company, they likely understand that top candidates have multiple options and move quickly to extend offers to strong performers.

Does Baseten require a cover letter with applications?

Baseten's Ashby-powered application forms may include an optional field for additional notes or a cover letter, but formal cover letters are not typically required for startup engineering roles. That said, a brief, targeted note (3-4 sentences) explaining why you're specifically interested in AI infrastructure and Baseten's approach to model serving can differentiate you from candidates who submit a resume alone. Focus on what draws you to the problem space — mention Truss, their inference platform, or a specific technical challenge you'd want to tackle — rather than writing a traditional full-page cover letter.

What resume format works best with Baseten's Ashby ATS?

Ashby is a modern ATS with strong parsing capabilities, but you'll still get the best results with a clean, single-column PDF format. Use standard section headers (Experience, Education, Skills), avoid tables or multi-column layouts, and keep graphics to a minimum. Ensure your name, contact information, and LinkedIn/GitHub URLs are in the body of the document, not in headers or footers that parsers sometimes skip. Ashby will extract your information into a structured candidate profile, so consistency and clarity in formatting help ensure nothing gets lost in translation.

What technical skills should I emphasize when applying to Baseten engineering roles?

The specific skills depend on the role, but Baseten's engineering positions consistently value expertise in Python, Go, or Rust for systems-level programming, deep familiarity with Kubernetes and container orchestration, GPU computing knowledge (CUDA, TensorRT), and experience with ML model serving frameworks. For roles like GPU Kernel Engineer, low-level optimization skills and CUDA kernel development are essential. For Model API roles, strong API design, gRPC/REST expertise, and understanding of inference optimization patterns matter most. Across all engineering roles, experience with cloud GPU infrastructure (AWS, GCP) and distributed systems design is highly relevant.

Can I apply to multiple roles at Baseten simultaneously?

Yes, Ashby allows you to apply to multiple positions, and Baseten's recruiting team can see your complete application history across roles. However, apply strategically rather than broadly — submitting to 5+ roles can signal uncertainty about your direction. If you're genuinely qualified for two or three related positions (for example, Software Engineer - Model APIs and Software Engineer - Model Performance Tooling), tailor your resume for each application to highlight the most relevant experience. A brief note explaining your interest in each specific role can help recruiters route your application effectively.

Does Baseten hire remote employees, or is the role in-office?

Baseten has typically operated with a hybrid or remote-friendly model, though specific roles may have location preferences — particularly for roles that benefit from close collaboration like Forward Deployed Engineering. Check each job posting carefully for location requirements, as they can vary by team. San Francisco is Baseten's headquarters, and some roles may prefer or require Bay Area presence. The company's investment in roles like Engineering Manager for Forward Deployed Engineering (LLM) suggests they value both in-person collaboration for certain functions and distributed work for others.

How should I prepare for a Baseten systems design interview?

Baseten's product is a model inference platform, so systems design questions will likely center on GPU infrastructure, model serving, and distributed systems challenges. Prepare by thinking through scenarios like: designing an autoscaling system that handles bursty inference traffic, implementing a model caching layer that minimizes cold starts for large language models, or building a multi-tenant GPU scheduling system. Familiarize yourself with Baseten's Truss framework, read their engineering blog posts, and understand the tradeoffs between different inference engines (vLLM, TensorRT-LLM, Triton). Practicing with real-world constraints — cost optimization, latency SLAs, GPU memory management — will be far more valuable than rehearsing generic systems design patterns.

What level of AI/ML experience does Baseten expect from non-engineering candidates?

For go-to-market roles like Account Executive, Program Manager, or Technical Enablement Lead, Baseten expects strong domain literacy rather than hands-on ML engineering experience. You should understand the ML model lifecycle, be familiar with concepts like inference versus training, know what GPU infrastructure involves, and be able to speak credibly about the challenges engineering teams face when deploying AI models to production. Prior experience selling to or working with ML engineering teams, DevOps/infrastructure buyers, or developer tool customers is highly valued. Reading Baseten's blog, understanding their customer use cases, and being conversant in the competitive landscape (Replicate, Modal, SageMaker) will demonstrate the domain depth they need.

What should I include in my application if I don't have direct ML infrastructure experience?

If you're coming from adjacent domains — cloud infrastructure, backend engineering, developer tools, or general ML engineering — focus on the transferable systems skills that Baseten values. Experience with high-throughput API design, Kubernetes at scale, distributed databases, or performance optimization translates directly to model serving challenges. Highlight any exposure to ML workflows, even tangential: building data pipelines, deploying models as microservices, or managing GPU workloads in the cloud. Frame your experience in terms of the problems Baseten solves — scalability, reliability, performance, developer experience — and articulate your genuine interest in the AI infrastructure space. A candidate with strong systems fundamentals and authentic curiosity about ML deployment is often more compelling than someone with shallow ML experience and no infrastructure depth.

Sample Open Positions

Sources

Baseten Careers Page — Baseten
Baseten Company Overview and Product Information — Baseten
Truss: An Open-Source Model Serving Framework by Baseten — GitHub
Ashby ATS Platform - Recruiting Software for Growth Companies — Ashby
Baseten Company Profile and Reviews — Glassdoor