Forward Deployed Engineer

Description

The company builds specialized, efficient language models meant to outperform large general-purpose models on specific tasks. The team includes former members of Google Research, Apple, Stanford, and Cambridge.

The company's open source GLiNER models have been downloaded over 50M times and are used by teams at NVIDIA, Meta, and Airbnb.

The company has raised $25M, covered by TechCrunch, from Microsoft, Khosla Ventures, Insight Partners, GitHub CEO Thomas Dohmke, Docker CEO Scott Johnston, and others.

Responsibilities

• You work inside customers' stacks to get models running in production. The role suits someone who enjoys hard technical problems and working directly with the people who face them.
• Work alongside customer engineering teams to scope, build, and deploy the company models into their production systems.
• Write production code for integrations, data pipelines, eval harnesses, and services that run on customer infrastructure, whether cloud, VPC, or on-prem.
• Fine-tune and evaluate models on customer data, showing gains over the baseline in accuracy, latency, and cost.
• Troubleshoot hard issues such as auth, networking, GPU and inference performance, data quality, and unusual edge cases.
• Own the technical side of deals from the first call through go-live, working closely with the founders and account executives.
• Turn field work into reusable tools, reference architectures, and SDK improvements.
• Relay customer feedback to research and product, and advocate for what customers need.

Requirements

• A BS or MS in Computer Science or a related field, or equivalent experience.
• 3+ years as a software engineer. Customer-facing experience is preferred, such as forward deployed engineering, solutions engineering, consulting, or similar.
• Strong Python skills, plus comfort with at least one of TypeScript, Go, or Rust.
• Hands-on experience shipping LLM or ML systems to production, including APIs, fine-tuning, evals, RAG, or agents.
• Solid working knowledge of cloud and infrastructure basics: AWS, GCP, or Azure, Docker, Kubernetes, networking, and security.
• Able to explain tradeoffs clearly to both an engineer and a VP in the same meeting.
• You take ownership and fix problems yourself rather than filing a ticket.
• Bonus: experience with GLiNER, NER, or other information extraction models.
• Bonus: experience at an early-stage startup.
• Willingness to travel to customer sites as needed.