Member of Technical Staff, Backend, LLM Applications at Inception (Bay Area)

Inception· Bay Area· $200k–$350k·4d ago

Role details

Salary

$200k–$350k

Work type

Onsite

Employment

Full-Time

Equity

Yes

Job description

The Role

We seek experienced backend engineers to own the systems that serve our diffusion LLMs in production. You'll build and operate infrastructure that handles billions of inference requests — optimizing for latency, throughput, cost, and reliability. This role sits at the intersection of ML systems and backend infrastructure.

Key Responsibilities

Design, build, and operate scalable backend services and model serving infrastructure for our diffusion LLMs.
Implement and manage load balancing, autoscaling, and traffic routing for model endpoints.
Build systems for model versioning, canary deployments, and zero-downtime rollouts.
Develop monitoring, alerting, and observability tooling to ensure SLA compliance and rapid incident response.
Benchmark and evaluate serving frameworks and hardware configurations to inform infrastructure decisions.

Qualifications

BS/MS/PhD in Computer Science or a related field (or equivalent experience).
5+ years of experience building production backend systems.
Strong proficiency in Python, including async programming and concurrent systems.
Solid understanding of distributed systems, networking, and load balancing at scale.
Familiarity with Kubernetes, CI/CD pipelines, and cloud infra (AWS and/or Azure).

Preferred Skills

Experience serving LLMs or other large generative models in production at scale.
Experience with cloud infrastructure (AWS, Azure), including GPU instance management and cost optimization.
Experience with infrastructure as code tools (Terraform) and deployment automation.
Experience with monitoring and observability tools (Prometheus, Grafana).
Familiarity with model serving frameworks (vLLM, Triton Inference Server, TensorRT-LLM).

Why work at Inception

Culture: A tight‑knit team of scientists, engineers, and builders focused on shipping frontier AI research. Emphasis on innovation, speed, and real‑world impact.
Work policy: In‑office for most roles (Palo Alto HQ); a Marketing Intern position is listed as remote. Typical time on‑site is full‑time in the office.
Notable perks / engineering culture: Opportunity to work on cutting‑edge diffusion LLMs from scratch, contribute to foundational research (papers published), and collaborate with alumni from top AI labs. Roles span from kernel engineering to RL infrastructure and product management.
Growth: Rapidly scaling headcount (+187% YoY) with open positions across multiple disciplines, offering strong career progression in a high‑visibility startup.

About Inception

Company Overview

One-liner: Inception is an AI research and product company building diffusion-based language models (dLLMs) for production applications, offering 5x faster inference than traditional autoregressive LLMs.
Entity Type: Private (Seed-stage; $57M total funding)
Headquarters: Palo Alto, California, United States
Founded: Not publicly available (earliest funding round dated March 2025)
Founders: Stefano Ermon (CEO), Aditya Grover (Co-Founder & CTO), Volodymyr Kuleshov (Co-Founder)

Core Business

Primary industry: Artificial intelligence research and infrastructure; large language models (LLMs)
Target customers: B2B developers, enterprises, and AI application builders requiring low-latency, high-volume LLM inference for multi-step agents and real-time products.
Mission or purpose: “We’re building the next generation of LLMs” – enabling intelligent, responsive, and scalable AI applications through diffusion-based architectures.

Products & Services

Mercury (dLLM family): The world’s first commercially available family of diffusion large language models. Generates output via a coarse-to-fine iterative refinement process over a small number of steps, achieving 5x greater speed than autoregressive models while maintaining best-in-class quality. Seamlessly integrates into existing LLM workflows.
Research technologies: d1 Reasoning, Discrete Diffusion Guidance, Direct Preference Optimization, Flash Attention, Decision Transformers, and Diffusion Models – foundational contributions that underpin the company’s product.

Market Standing

Valuation / Market Cap: Not disclosed
Key Metric: Total funding of $57M across two seed rounds (2025)
Notable Investors / Partners: Menlo Ventures (led the $50M seed round), Amazon Web Services (non‑equity assistance), Eric Schmidt (individual investor)
Growth Signals: Headcount grew 187.5% year‑over‑year to 39 employees; LinkedIn followers increased 240% yearly to ~11,800; active job postings across AI systems, engineering, research, product, and marketing; talent sourced from Google DeepMind, Stanford SAIL, Meta, Amazon, and Pixxel.

Competitive Advantages

Diffusion paradigm: Non‑autoregressive generation that allows parallel refinement and revision during inference, breaking the sequential token‑by‑token bottleneck of conventional LLMs.
Speed‑cost curve: Delivers 5x faster output with a fundamentally different cost structure, critical for real‑time, multi‑step agent workflows.
Founding team pedigree: Co‑founders have pioneered breakthroughs in model architectures (Flash Attention, Direct Preference Optimization) and have deep experience turning research into production systems at scale.

Strategic Focus

Scaling Mercury adoption by integrating dLLMs into existing LLM pipelines and targeting high‑volume, latency‑sensitive applications.
Advancing research in diffusion‑based reasoning, reinforcement learning for language models, and training/serving infrastructure.
Expanding the team across AI systems (kernels, inference, training infra), product engineering, and research to accelerate product‑market fit.

Sources