What you'll do:
Build and operate the internal application platform that lets employees create and deploy AI-generated applications on curated backend and frontend frameworks, with starter templates, design guidance, authentication, and secure deployment built in, so app authors own only business logic, user flows, and UI.
Give those applications governed access to data, including connectivity to our data warehouse and a shared Postgres cluster for application state that is automatically integrated into our data platform.
Build and operate a central knowledge platform over unstructured content (documents, slide decks, meeting transcripts, Slack) and structured data from battery test stands, manufacturing robots, and deployed systems, pairing document retrieval with query access to structured data through semantic models and text-to-SQL. Own its retrieval quality through semantic and hybrid search, re-ranking, chunking, and classification, with source-level permissions and evaluation sets that show whether a change helps.
Design and deploy the shared MCP infrastructure, including a gateway layer (such as agentgateway) for authentication, authorization, rate limiting, and audit logging, and build MCP servers for applications and services across the software org and for selected external tools.
Own model selection and cost: evaluate open-source and open-weight models against frontier models on our own tasks, decide where each fits, and reduce token usage through context management, caching, and model routing.
Build the agent orchestration layer, including multi-agent workflows and long-running loops with checkpoints, human review gates, and automated evaluation. Work with data engineers to decide when work should run as a deterministic, scheduled workflow and when it needs a graph or state machine in which an agent chooses among bounded steps.
Partner with product R&D, battery design, manufacturing process engineering, product costing, and root cause analysis teams to find repeatable work and automate it, including agent workflows whose output informs critical design and process decisions.
Instrument the platform with tracing, evaluation, and usage and cost attribution by team, and stand up the AWS infrastructure and infrastructure-as-code (Pulumi and/or Terraform) it runs on.
Help stand up a company-wide skills and plugin marketplace that vets contributions against AI policy and scans them for security vulnerabilities with open-source and commercial tools. Advise on AI governance, recommending changes that make the policy specific to platform needs and automating its execution, such as approvals for new tools.
Raise the engineering bar through design docs and RFCs, code review, mentorship, and technical influence that reaches beyond the immediate team.
What you'll bring:
Bachelor's degree in computer science, software engineering, computer engineering, or a related field plus 7+ years of software or data engineering experience, including building and operating production platforms or services that other engineers depend on.
Strong production programming in Python and Go, with experience building APIs and backend services.
Hands-on experience building LLM-powered systems in production, including retrieval-augmented generation, embeddings, vector and hybrid search, re-ranking, and structured-data access through text-to-SQL or semantic layers.
Experience with agent frameworks, tool calling, and the Model Context Protocol, and with workflow orchestration or state-machine frameworks such as LangGraph, Temporal, or Dagster.
Experience building evaluation sets and harnesses for LLM systems and measuring quality, latency, and cost across model providers, including open-weight models.
Experience with PostgreSQL and a cloud data warehouse, plus AWS, containers, CI/CD, and infrastructure-as-code with Pulumi and/or Terraform.
Working knowledge of platform and AI security: authentication and authorization (OAuth/OIDC), secrets management, sandboxed execution, tenant isolation, supply-chain scanning, and risks specific to LLM tools such as prompt injection and data exfiltration.
A track record of building internal platforms that other teams adopt, and of independently leading the design of novel solutions through technical design docs and RFCs.
Excellent communication and organizational skills, including with scientists, engineers, and operators who aren't software developers, and comfort participating in an on-call rotation.
Nice to have: React and design-system experience; Snowflake or Databricks; serving open-weight models (for example with vLLM); API or AI gateway experience; AI governance or compliance frameworks (SOC 2, ISO 27001); background in battery, energy, manufacturing, or other physical-systems data.
#LI-Hybrid
Humanity is a cornerstone of Form Energy’s culture, and we make sure our compensation and benefits reflect that. Form Energy offers competitive salaries, stock options, and a holistic benefits package to ensure all employees have what they need to thrive while working here.
When it comes to you and your family’s health, we cover 100% of medical, dental, and vision premiums for full-time employees - and 80% of healthcare premiums for dependents. This starts from day one. We also offer at least 12 weeks of paid leave for new parents (up to 20 weeks for birthing parents), and generous vacation policies to give employees time to recharge when needed.
To build America’s energy future, we need everyone at the table. We are proud to be an equal opportunity employer, and encourage candidates from all backgrounds to apply to our open jobs.
If you may require reasonable accommodations to participate in our interview process, please contact accommodations@formenergy.com. Requests for accommodations will be treated with discretion.
Form Energy is committed to maintaining the privacy of our applicants. Please be aware that we will never solicit sensitive personal information such as Social Security numbers or bank account details during the recruiting or hiring process.