Forward Deployed Engineer
TribalScale
- New York, United States
- Mid Level
- Posted
Skills
Required
- AI
- tooling
- controls
- Python
- TypeScript
- APIs
- agent frameworks / workflow engines / coding agents / custom model-orchestration systems
- agentic workflows
- harness engineering
- AWS / Microsoft Azure / Google Cloud
Preferred
- retrieval-augmented generation
- embeddings
- vector search
- knowledge graphs
- fine-tuning
- model-routing
- AI infrastructure
- evaluation tooling
- open-source
- Kubernetes
Join TribalScale as a Forward Deployed Engineer, where you'll work at the intersection of software engineering, artificial intelligence, product development, and client delivery. You'll embed with client and internal teams to understand complex business problems, design solutions, and deploy production-quality software. This role requires a strong proficiency in Python, TypeScript, or another language commonly used to build AI applications, as well as excellent communication skills and experience in building and deploying production-grade software.
Partner directly with clients to understand their objectives, workflows, technical environments, data, and operational constraints, and translate ambiguous business challenges into clear technical requirements.
About TribalScale
You thrive in challenging environments where innovation is key. Your passion for technology is matched only by your drive to redefine its limits. You seek more than a job; you seek a calling—a chance to make a lasting impact on the digital landscape
Strong product judgment and the ability to balance model capability, reliability, latency, cost, security, and user experience
Responsibilities
- Design and build AI-enabled applications, agentic workflows, developer tools, APIs, data pipelines, and full-stack products, and engineer the harnesses around AI models.
- Collaborate with product managers, designers, engineers, data specialists, security teams, and client stakeholders throughout the delivery lifecycle, and establish strong engineering practices.
- Understanding of the trade-offs involved in prompts, context management, memory, retrieval, tool use, structured outputs, and model selection
- Experience instrumenting AI systems to trace decisions, inspect intermediate states, measure quality, and diagnose failures
- Experience designing evaluation systems for AI applications, including task-based benchmarks, regression suites, model-graded evaluations, human review, and production monitoring
- Experience collaborating directly with customers, stakeholders, or cross-functional delivery teams
- Understanding of AI security concerns, including prompt injection, data leakage, excessive agency, insecure tool use, and supply-chain risks
Requirements
- A practical understanding of harness engineering: designing the infrastructure, context, tools, permissions, controls, and feedback mechanisms that enable AI agents to operate reliably
- This role is ideal for an adaptable engineer who enjoys direct customer interaction, can move comfortably between discovery and delivery, and wants to see their work create immediate, measurable impact
- Strong proficiency in Python, TypeScript, or another language commonly used to build AI applications, platforms, and developer tooling
- Excellent written and verbal communication skills, including the ability to explain AI-system behavior, limitations, risks, and technical trade-offs clearly
- Familiarity with graph-based orchestration, state machines, and multi-step workflows for coordinating agents, tools, data, and human approvals
- Ability to move from an ambiguous customer problem to a working prototype, validate it with real users and evaluations, and evolve it into a secure, maintainable production system
- We recognize that strong candidates may not meet every qualification listed. If this role excites you and your experience is adjacent to what we have described, we encourage you to apply
- A high degree of ownership, curiosity, empathy, and bias toward action
- Experience building or working with agent loops, including planning, tool selection, execution, observation, reflection, recovery, and termination
- Ability to travel occasionally for client workshops or on-site delivery, where required
- Professional experience building and deploying production-grade software, ideally including AI-powered or agentic systems
- Strong knowledge of APIs, distributed systems, databases, cloud infrastructure, and modern software-development practices
- Ability to quickly understand unfamiliar systems, technologies, and business domains
- Comfort working in fast-moving environments where requirements and technical constraints continue to evolve
- A history of taking products from initial discovery through production launch and ongoing adoption
- Experience building systems that use multiple models or specialized agents to complete complex tasks
- Experience creating tool interfaces, execution sandboxes, permission models, and human-in-the-loop controls for autonomous or semi-autonomous systems
- Experience with cloud platforms such as AWS, Microsoft Azure, or Google Cloud
- Experience with agent frameworks, workflow engines, coding agents, or custom model-orchestration systems
- Experience with agentic AI and harness-driven development, including designing, building, and optimizing autonomous AI agents, multi-agent workflows, tool integrations, and agent harnesses that support reliable, scalable, and production-ready AI applications
- Claude Certified Architect - Foundations or Professional certification (or equivalent), with practical experience designing and implementing Claude-based AI solutions, agentic workflows, and enterprise integrations
- Experience in consulting, professional services, solutions engineering, sales engineering, or another customer-facing technical role
- Familiarity with AI observability, including traces, token and cost analysis, latency measurement, failure classification, and quality dashboards
- Experience evaluating AI systems using golden datasets, simulators, adversarial testing, online experiments, and production feedback
Preferred
- Familiarity with retrieval-augmented generation, embeddings, vector search, knowledge graphs, fine-tuning, and model-routing strategies
- Contributions to AI infrastructure, evaluation tooling, agent frameworks, or related open-source projects
- Experience with containers, Kubernetes, infrastructure as code, and CI/CD tooling
- Experience working in regulated or complex enterprise environments
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