--- title: 'Backend Engineer at Bespoke Labs' canonical: 'https://feeny.ai/job/backend-engineer-bespoke-labs-mountain-view-2wfkmwxgk7se' type: 'job' last_seen: '2026-09-10' --- # Backend Engineer at Bespoke Labs - **Company:** Bespoke Labs - **Location:** Mountain View, CA - **Employment:** full-time - **Work type:** hybrid - **Posted:** 2026-05-27 - **Last confirmed live:** 2026-09-10 - **Apply:** https://jobs.ashbyhq.com/bespokelabs/db976a5b-c899-46ec-9f78-4a1a6409e118 ## Job description ## About Bespoke Labs Bespoke Labs is an applied AI research lab pioneering data and RL environment curation for training and evaluating agents. Recently, we curated Open Thoughts, one of the best open reasoning datasets used by multiple frontier labs, trained SOTA specialized models such as Bespoke-MiniChart-7B and Bespoke-MiniCheck, and built the environment infrastructure that frontier labs and enterprises use to make their agents reliable. Bespoke is uniquely positioned to capture a large share of data and RL environment curation. ## About the Role We're looking for an Infrastructure Engineer to own the execution layer beneath our RL environments: the systems that let an agent operate inside a realistic, multi-tool world coherently for hours or days. This is a hard systems problem disguised as an AI job. As the tasks agents can complete keep lengthening, the environments that train them have to stay coherent across far longer horizons than anything that exists today. That means sandboxing and isolation you can trust, execution that's fast and cheap enough to run at training scale, and the ability to snapshot, restore, inspect, and branch a running environment instead of treating every rollout as one-shot. You'll build the platform that makes all of this possible. You'll work closely with our research and data teams, and directly with frontier labs and enterprise customers, to turn environment designs into infrastructure that runs reliably in production. ## What You'll Do - Environment Execution & Sandboxing: - Design and own the sandboxing and execution layer that environments run inside. Build systems to snapshot and restore environment state (disk, process, and where relevant memory and accelerator state) so runs can be paused, resumed, inspected, and branched rather than executed once. - Develop the machinery to detect failure modes early in a rollout (reward hacks, infra faults, fairness issues) and to revert to a known-good state, patch, and continue. - Extend execution to long-horizon and multi-node environments, where an agent operates across many tools and services over hours or days. - Performance & Scale - Own the performance characteristics of the platform: throughput, latency, and cost-per-rollout at scale. - Drive utilization and scheduling so we can run far more environment rollouts per dollar without sacrificing reliability. - Profile and remove bottlenecks across the stack, from container startup to environment teardown. - Build the observability that lets us understand what's happening inside thousands of concurrent, long-running rollouts. - Environment Platform - Build and maintain the framework for specifying, packaging, and deploying RL environments which is used by both humans and agents authoring environments internally. - Create the tooling that lets researchers and environment authors debug a specific failure across hundreds of long agent traces. - Collaboration & Production Excellence - Scale prototypes into production systems with reproducible workflows and high engineering standards. - Write the documentation and tools that let internal teams and external users build on the platform. ## What We're Looking For - Systems & Infrastructure - Strong track record building production systems or research infrastructure at scale: distributed systems, execution engines, container/sandboxing infrastructure, or similar. - Deep comfort with the systems layer: containers and isolation (e.g. namespaces, cgroups, VMs, gVisor/Firecracker-style sandboxing), filesystems, process and state management. - Experience making systems fast and cheap — profiling, scheduling, resource utilization, and cost optimization at scale. - Proficiency with cloud platforms (GCP, AWS) and distributed computing. - Strong engineering fundamentals and a systematic approach to testing, validation, and reliability. - Execution & Ownership - Comfort operating in ambiguity. - Strong Python skills; comfort in a systems language (Rust, Go, or C++) is a plus. - Ability to use modern tools such as Claude Code effectively. - Collaboration & Communication - Excellent communication skills for working with research teams and enterprise customers. - Ability to translate between research needs and infrastructure requirements. - Comfortable presenting technical work to diverse audiences. ## Nice to Have Experience with RL training or evaluation infrastructure, or the execution layer for agent rollouts. Experience with checkpoint/snapshot-restore systems, CRIU, or distributed state management. Background in high-throughput, low-latency execution systems. Contributions to widely-used infrastructure, datasets, benchmarks, or open-source systems. Previous experience in a research engineering or infrastructure role at an AI or systems-heavy company. Logistics Location: Mountain View, CA Compensation: Competitive salary and equity Benefits: Health coverage, and the opportunity to work directly with the world's leading AI research labs ## About Bespoke Labs ## Company Overview - **One-liner**: Bespoke Labs is an applied AI research lab building company-scale RL environments and data curation infrastructure to train reliable, production-grade AI agents. - **Entity Type**: Private (Seed stage) - **Headquarters**: Mountain View, California, United States (with an office in Santa Clara, CA) - **Founded**: 2024 - **Founders**: Mahesh Sathiamoorthy (CEO, formerly Google DeepMind) and Alex Dimakis (CSO, Professor at UC Berkeley) ## Core Business - **Primary industry**: Applied AI Research / Software Development (Agent Infrastructure) - **Target customers**: B2B — Frontier AI labs (e.g., Anthropic, OpenAI, Google DeepMind) and enterprises needing to train, evaluate, and optimize complex, long-horizon AI agents for production. - **Mission**: To build the environment infrastructure for the agent revolution, making AI agents dependable in production by creating entire digital worlds for training. ## Products & Services - **Company-Scale RL Environments**: Infrastructure that simulates real codebases and microservices, allowing agents to master complex, long-horizon workflows required for production. - **Agent Evaluation & Optimization (GEPA)**: An evolutionary algorithm (GEPA optimizer) that automates prompt and policy searches, achieving superior accuracy faster than manual prompt engineering. Over 200 teams use GEPA in production. - **Production-Grade RL & Benchmarking**: Collaborative research in RL, data curation, and benchmarking to ensure training environments keep pace with the advancing frontier of model capabilities. - **OpenThoughts Dataset**: One of the best open reasoning datasets (10k+ monthly downloads on Hugging Face), cited in ICLR 2026. - **Terminal-Bench**: The first environment-based benchmark for agentic systems, cited by Anthropic, OpenAI, and Google DeepMind, published at ICLR 2026. - **Bespoke-MiniCheck & Bespoke-MiniChart**: SOTA models developed in-house for fact-checking and chart understanding. ## Market Standing - **Valuation**: Not publicly disclosed. - **Total Funding**: $8.25M (Seed Round, June 2024). - **Notable Investors/Partners**: Lead investor is **8VC**. Advisors include Tasso Argyros (VP of Eng at Databricks, ex-CEO of ActionIQ), Joseph Gonzalez (Associate Professor at UC Berkeley, creator of LMSYS/vLLM), and Greg Durrett (Associate Professor at NYU). Cited by Anthropic, OpenAI, and Google DeepMind. - **Growth Signals**: Headcount grew **+238.5% year-over-year** (from ~8 to 28 employees). Monthly headcount growth is **+33.3%**. Active job postings increased **+183.3% quarterly** (34 open positions). Over 200 teams use GEPA in production. OpenThoughts sees 10k+ monthly downloads. ## Competitive Advantages - **First-Mover in Agent Environments**: They identified the bottleneck for agent reliability is the environment, not the model, and are building the foundational infrastructure for this new paradigm. - **SOTA Research Output**: Published work at ICLR 2026 (Terminal-Bench, GEPA, OpenThoughts) that is being cited and used by frontier labs (Anthropic, OpenAI, Google DeepMind). - **Strong Academic & Industry Ties**: Founded by a Google DeepMind alum and a UC Berkeley professor, with advisors from Databricks, UC Berkeley, and NYU. Team includes alumni from Google, Scale AI, Microsoft, and AI2. - **Open Source Influence**: OpenThoughts is a widely used open reasoning dataset, building community credibility and attracting top talent. ## Strategic Focus - **Scale the Environment Infrastructure**: Build increasingly complex and realistic digital worlds for training agents, moving beyond demos to production-grade reliability. - **Expand Enterprise Adoption**: Help enterprises evaluate and optimize agents in environments that mirror their specific systems and processes. - **Continue Frontier Research**: Push the boundaries of RL, data curation, and benchmarking to maintain a lead in the agent training space. - **Talent Acquisition**: Aggressively hiring researchers and engineers (34 open roles) to scale the team and the product. ## Why Work Here - **High-Impact, Cutting-Edge AI Work**: Opportunity to work on one of the most important problems in AI — making agents reliable. The team is described as "exceptional researchers and engineers." - **Strong Research Culture**: The lab publishes at top venues (ICLR) and contributes to open source (OpenThoughts). The work is a blend of product engineering and fundamental research. - **Rapid Growth Stage**: With 238% YoY headcount growth and a recent seed round, this is an early-stage opportunity to shape the company's culture and technical direction. - **Office-First, Collaborative Environment**: Based in Mountain View/Santa Clara with an emphasis on in-person collaboration. The team is small (28 people) but growing fast. - **Notable Alumni & Hiring Sources**: The team attracts talent from Scale AI, Berkeley RISE Lab, Google DeepMind, and Grammarly, indicating a high-caliber peer group. - **Open Roles**: Actively hiring for GPU/CUDA Engineers, Product Engineers, Scientific ML Engineers (PhD Interns), and Quantitative Financial Specialists, among others. ## Sources 1. [bespokelabs.ai](https://www.bespokelabs.ai/) 2. [bespokelabs.ai/about-us](https://www.bespokelabs.ai/about-us) 3. [linkedin.com/company/bespokelabsai](https://www.linkedin.com/company/bespokelabsai) 4. [pitchbook.com/profiles/company/651710-80](https://pitchbook.com/profiles/company/651710-80) 5. 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