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Featherless AI

Founding Sales Engineer

Remote, Canada

Pay: $150,000 to $190,000 USD / year
Job type: Full-time
Workplace: Remote
Posting language: English
Posted: Sep 18, 2026

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Original job description

About Featherless

Featherless.ai is building the world's most reliable open-model inference platform.

Backed by AMD, Airbus Ventures, 500 Global, Kickstart Ventures, HF0 Residency, Panache Ventures, and Oakseed Ventures, we're a well-funded team of researchers and engineers on a mission to democratize AI through performance and accessibility.

Our cloud provides instant access to 40,000+ open-source AI models and the research innovations powering next-generation efficiency, reliability, and model optimization.

About the Role

We're hiring our first Sales Engineer to pair directly with our founding Account Executive in North America. You are the technical half of a two-person deal team: the AE owns the commercial motion, you own the technical win.

Our buyers are developers, ML engineers, and CTOs, and they don't buy on slides. They buy when someone credible sits with them, looks at their workload, and shows them that open models on Featherless are faster, cheaper, and more reliable than what they're running today. That's your job - hands on keyboard, in the customer's stack, from first technical call through production rollout.

Because you're the first SE here, you'll build the function as you go: the demo environment, the POC playbook, the benchmark harness, the security answers, the migration guides. Every deal you win should make the next one easier to win.

If you like being the person who can actually answer the hard question in the room - and want the technical foundation of a Series A GTM engine to be yours - this is for you.

What You'll Own Immediately

Partner with the founding AE as the named technical owner on every active opportunity in North America
Run technical discovery: map each prospect's models, workloads, latency and throughput targets, spend, and constraints
Design and drive POCs and benchmarks that prove Featherless against the incumbent - closed-model APIs, another inference provider, or self-managed GPUs
Build the demo environment and reusable technical collateral that the whole GTM team sells with
Own technical objection handling end-to-end: performance, reliability, cost modeling, security, and data handling
Be the field's voice into product and engineering - you'll know what we're losing on before anyone else does

Core Responsibilities

Lead technical calls tailored to each buyer's stack: architecture reviews, live demos, and working code against their real use case
Build migration paths off closed-model APIs and onto open weights - model selection, evaluation, prompt and output parity, cutover plan
Run benchmarks and produce the throughput, latency, and cost-per-token analysis that anchors the business case the AE builds
Scope and execute POCs with clear technical success criteria, then hold the customer and us to them
Write the technical sections of proposals, RFP responses, and security questionnaires; keep a reusable answer library so we never write the same answer twice
Support onboarding and first production workloads, then hand off cleanly and stay available for expansion
Feed structured field input to product and engineering - feature gaps, model coverage requests, reliability issues, competitive intel
Build and maintain the technical assets that scale the team: demo apps, notebooks, reference architectures, integration guides, internal enablement for BDRs and future AEs
Represent Featherless technically at conferences, meetups, and developer events
Keep POC and technical-stage detail current in HubSpot so forecasting reflects technical reality, not optimism

What You Bring

3–6 years in pre-sales engineering, solutions architecture, or a forward-deployed / customer-facing engineering role - at a GPU cloud, inference provider, MLOps platform, AI/developer tooling company, or cloud infrastructure vendor
Genuinely hands-on: you write Python comfortably and build your own demos rather than requesting them
Working knowledge of modern LLM inference - serving stacks (vLLM, SGLang, TensorRT-LLM or similar), OpenAI-compatible APIs, quantization, LoRA and fine-tune serving, batching and KV cache behavior, and what actually drives tokens/sec and cost
Practical familiarity with the open-model ecosystem: Hugging Face, the major open weight families, and how teams evaluate one model against another
Comfortable with containers, Kubernetes, and cloud networking and security fundamentals
Credible with both audiences in the same meeting - the ML engineer who wants the numbers and the CTO who wants the risk and cost story
Strong written communication: your benchmark writeups and architecture docs should be good enough to forward to a buyer's CEO
Entrepreneurial and self-directed - comfortable being the first SE, with no playbook and no one to escalate the technical answer to
Use AI tools heavily in your own workflow to research, prototype, and move faster

Nice to have: experience selling or building on AMD GPUs / ROCm; exposure to enterprise security and compliance review; open-source contributions or public technical writing; experience as the first technical hire on a GTM team.

Why Featherless

Build the sales engineering function at a company in one of the fastest-moving spaces in AI
Work as a true pair with the founding AE, and directly with the CRO and founders - small team, no layers, immediate impact
Real technical depth: 40,000+ open models and an in-house research team shipping inference and optimization work you'll get to sell
Be part of a small, high-performing team making open weight AI accessible to everyone
Competitive base + variable tied to the team's number, plus equity

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More about this job

Responsibilities

The Sales Engineer will partner with the founding Account Executive to lead technical discovery, design POCs, and drive benchmarks for AI model inference. They are responsible for building technical collateral, handling objection management, and providing field feedback to the product and engineering teams.

Requirements

Candidates must have 3–6 years of experience in pre-sales engineering or solutions architecture within the AI, cloud, or MLOps space. Proficiency in Python, modern LLM inference stacks, and container orchestration is required, along with strong communication skills to engage both technical and executive stakeholders.

Skills

Sales Engineering
Python
LLM Inference
vLLM
SGLang
TensorRT-LLM
OpenAI-compatible APIs
Quantization
LoRA
Fine-tune serving
Kubernetes
Cloud networking
Technical discovery
Benchmark analysis
Objection handling
Pre-sales

Benefits

Competitive base salary
Variable compensation
Equity

Remote locations

Ashburn, United States
Atlanta, United States
Austin, United States
Boston, United States
Charlotte, United States
Chicago, United States
Dallas-Fort Worth, United States
Denver, United States
Fairfax, United States
Halifax, Canada
Miami, United States
Montreal, Canada
Nashville, United States
New York City, United States
Orlando, United States
Ottawa, Canada
Phoenix, United States
Raleigh, United States
Salt Lake City, United States
Scottsdale, United States
Seattle, United States
Tampa, United States
Toronto, Canada
Vancouver, Canada
Washington, United States

Remote locations (standardized)

Ashburn, Georgia, United States
Atlanta, Georgia, United States
Austin, Texas, United States
Boston, Massachusetts, United States
Charlotte, North Carolina, United States
Chicago, Illinois, United States
Denver, Colorado, United States
Fairfax, Virginia, United States
Fort Worth, Texas, United States
Halifax, Nova Scotia, Canada
Miami, Florida, United States
Montreal, Quebec, Canada
Nashville, Tennessee, United States
New York, New York, United States
Orlando, Florida, United States
Ottawa, Ontario, Canada
Phoenix, Arizona, United States
Raleigh, North Carolina, United States
Salt Lake City, Utah, United States
Scottsdale, Arizona, United States
Seattle, Washington, United States
Tampa, Florida, United States
Toronto, Ontario, Canada
Vancouver, British Columbia, Canada
Washington, District of Columbia, United States

Visa sponsorship

Not detected in the job text

Keywords

Sales Engineering
AI
Inference Platform
LLM
Python
vLLM
SGLang
TensorRT-LLM
Quantization
LoRA
Kubernetes
Cloud Infrastructure
MLOps
HubSpot
POC
Benchmark
API
GPU
Open-source
Technical Sales
Solutions Architecture
Data Handling
Security
Compliance
Go-to-market

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