Engineering Manager, AI Platform
New
S
SequencingGenomics, AI
Remote USFull-TimeManager
Salary not disclosed
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Job Details
- Experience
- 8+ years of software engineering experience, including 3+ years managing or leading high-performing engineering teams
- Required Skills
- RESTful APIsPrompt EngineeringLLMGenerative AIDistributed Systems
Requirements
- 8+ years of software engineering experience.
- 3+ years managing or leading high-performing engineering teams.
- 2+ years of experience building production LLM, generative-AI, or agentic products.
- Experience designing or shipping systems involving tool use, orchestration, memory, retrieval, prompts or skills, evaluation loops, and human review.
- Technically credible across distributed systems, data pipelines, APIs, cloud infrastructure, and production reliability.
- Ability to engage deeply on system design and failure modes while empowering senior engineers.
- Experience building strong engineering cultures grounded in ownership, growth mindset, and direct feedback.
- Comfortable operating in POC and MVP phases, learning through prototypes, and evolving systems toward production scale.
- Understanding of how to balance speed with guardrails for high-consequence outcomes.
- Strong communication skills across cross-functional teams.
- Must be based in the United States.
Responsibilities
- Lead, coach, and develop the engineers responsible for Sequencing’s AI platform, creating clear ownership, tight feedback loops, and a high bar for technical quality.
- Translate approved product intent into sequenced engineering plans with clear owners, dependencies, risks, test criteria, and completion gates.
- Guide delivery across retrieval, memory, integrations, prompts and skills, agent orchestration, evaluation, observability, and supporting data systems.
- Partner with engineers and architecture owners on system design, technical tradeoffs, and boundaries between deterministic and probabilistic components.
- Build a rapid prototype-to-production loop that allows the team to learn quickly without compromising scientific accuracy, privacy, reliability, or maintainability.
- Establish evaluation as a release gate using deterministic validation, golden sets, LLM-as-judge methods, human annotation, scientific review, and clearly defined failure behavior.
- Improve prompt and configuration management, trace quality, latency, cost visibility, failure classification, and production observability.
- Create guardrails that enable engineers to move with autonomy, use AI-native development tools effectively, and take end-to-end ownership of their work.
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