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Lead AI Native Engineer

Updated

Pay not postedMilpitas, CAOn-siteStaff

Lead the hands-on architecture and production deployment of RoboForce’s vertical agents and shared agent foundation, reporting directly to the co-founder.

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View posting at RoboForce

At a glance

Posted base pay

Not posted

The work

Builds for teams inside the company

Balance

  1. Mostly building
  2. Mixed
  3. Mostly pre-sales or support

Project stages

  1. Pre-sales (not covered)
  2. Proof of concept (not covered)
  3. Implementation
  4. Operations
Coding
Writes production code
Builds for
Internal teams
Level
Staff+
Role
Technical lead

What they ask for

Experience

  • 5+ yearssoftware engineering, applied AI, ML systems, or a related field

Background

  • Software engineering

Tools

  • Claude Code
  • Codex
  • Cursor
  • MCP
  • agent SDKs
  • retrieval-augmented generation (RAG)
  • knowledge graphs
  • frontier model APIs
  • observability

Visa

  • Sponsors visas

Less common in FDE jobs

  • Who you build for: Internal teams14%
  • Company stage: Growth16%
  • Responsibility level: Staff+16%
  • Company size: About 51–200 people19%

Share of US FDE postings that state each fact.

The job description from RoboForce

Posted by RoboForce on

Why RoboForce

RoboForce is an AI robotics company developing Physical AI–powered Robo-Labor for dull, dirty, and dangerous work. The company’s robots are engineered for demanding industrial environments, with a focus on real-world deployment and scalability.

We are hiring a Lead AI Native Engineer to build RoboForce’s vertical agents and shared agent foundation. Reporting to the co-founder, you will turn frontier models into systems for strategy and engineering. This is a hands-on technical leadership role: you will write code, set architecture, and enable agent development—not lead People programs or organizational transformation.

Responsibilities

  • Build vertical agents. Own end-to-end agents for high-value workflows across research, software, hardware, data, and operations—from problem definition through production use.
  • Establish the agent foundation. Build reusable primitives for models, tools, orchestration, context, memory, retrieval, permissions, human approval, and long-running execution.
  • Create the context layer. Connect agents to trusted data through APIs, pipelines, MCP servers, and integrations with clear provenance and access control.
  • Own evaluation and reliability. Build benchmarks, regression tests, tracing, monitoring, and failure-analysis loops across quality, latency, cost, security, and resilience.
  • Advance frontier agent usage. Evaluate new models, coding agents, SDKs, and patterns, then turn useful capabilities into maintainable systems rather than demos.
  • Support strategic initiatives. Help company leadership apply agents and analytical systems to market and customer intelligence, partnerships, fundraising, diligence, scenario analysis, and executive decisions.
  • Provide technical leadership. Set architecture and engineering standards, review designs and code, and create reusable patterns for the technical team.

Requirements

  • 5+ years in software engineering, applied AI, ML systems, or a related field, with strong zero-to-one technical judgment.
  • Experience at a frontier AI lab, leading AI company, or comparable team working at the edge of current model capabilities.
  • A track record shipping production agentic systems that real users depend on—not only prompts, prototypes, or demos.
  • Deep experience with frontier model APIs, tool use, orchestration, context engineering, retrieval, memory, and multi-step workflows.
  • Experience building evaluations, regression tests, observability, and production failure-analysis loops for AI systems.
  • Exceptional fluency with AI-native development workflows using Claude Code, Codex, Cursor, agent SDKs, or equivalent systems, with a rigorous understanding of where agents work and fail.
  • Strong product judgment: able to turn an ambiguous decision or workflow into a useful, secure, measurable system.
  • Requires 5 days/week in-office collaboration with the team.

Bonus Qualifications

  • Experience with post-training, model evaluation, inference, or research infrastructure at a frontier lab or model company.
  • Experience with MCP infrastructure, developer platforms, knowledge graphs, RAG, or secure enterprise integrations.
  • Background in robotics, autonomous systems, industrial automation, or another technically complex physical-world domain.

Benefits

  • Competitive stock options/equity programs.
  • Health, dental, and vision insurance, 401(k) plan.
  • Visa sponsorship and green card support for qualified candidates.
  • Lunches and dinners, a fully stocked kitchen, and regular team-building events.

View posting at RoboForce

About RoboForce

Builds deployable Physical AI robot workers for demanding industrial environments, combining robot foundation models, edge compute, and a real-world deployment data flywheel.

Series B · 50–100 people

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Pay and conditions

Posted pay
Pay not posted
Location
Milpitas, CA
Work arrangement
On-site
Visa sponsorship
Available
Experience requirements
  • 5+ years: software engineering, applied AI, ML systems, or a related field (required)