Forward Deployed AI Engineer
About this role
Turing is focused on accelerating superintelligence to generate economic progress by working with frontier AI labs to create datasets, reinforcement learning environments, and research benchmarks. The company serves both the research side and enterprise clients by building and deploying end-to-end agentic AI systems within mission-critical workflows across various industries. Turing bridges the gap between frontier research and real-world deployment, using deployment signals to improve models and data quality.
The Forward Deployed AI Engineer role sits within the Enterprise AI business unit, which operates with the autonomy and focus of a scaling startup. This position is central to shaping how Turing applies AI to the workplace by solving complex, enterprise-grade problems for major clients. The engineer will be part of the initial team establishing this function, defining the practical application of advanced AI in business settings.
This role is designed for an individual who thrives in an intense, intellectually challenging environment where they can define the frontier of applied AI. The ideal candidate possesses strong business acumen and technical depth, capable of translating ambiguous client needs into concrete technical solutions and driving measurable business impact. It is suited for someone who enjoys high ownership and the responsibility of building solutions from initial discovery through to full production deployment.
What you will do
- Work directly with customers to identify high-value problems and translate complex business needs into clear technical direction.
- Architect and build production AI systems, encompassing multi-agent workflows, orchestration, tool use, context engineering, memory, and retrieval-augmented generation (RAG).
- Own the entire outcome for client solutions, leading work from discovery through deployment and ensuring measurable impact.
- Manage all aspects of system reliability, including evaluation, observability, security, latency, scalability, and cost.
- Develop and contribute to the playbook for Enterprise AI by applying expertise to establish processes where none exist.
- Collaborate closely with senior client leaders to ensure solutions meet demanding quality expectations.
What you need
- Possesses deep production engineering experience, typically requiring more than eight years building and operating sophisticated software systems.
- Must have expertise in core backend technologies such as Python, APIs, databases, distributed systems, and cloud infrastructure.
- Requires hands-on experience building production LLM and agent systems, including proficiency in orchestration, tool use, context engineering, memory, and state management.
- Demonstrated track record of owning complex systems end-to-end, covering architecture through deployment and scale.
- Needs a practical understanding of critical operational aspects such as evaluation, observability, security, reliability, latency, scalability, and cost.
- Must possess exceptional customer and technical leadership skills to navigate ambiguity and challenge assumptions constructively.
Nice to have
- Experience with enterprise workflows
Benefits
- Base salary of $200,000–$250,000
- Equity
Skills and tech stack
Questions candidates ask
What is the expected work model?
The work model is typically in-office, requiring three to five days per week, depending on client requirements. Candidates should expect to be based in the New York City area or within a reasonable commuting distance.
What is the compensation structure?
The compensation includes a base salary ranging from $200,000 to $250,000, plus equity. This structure reflects the high-impact nature of the role.
What is the primary focus of the role?
The primary focus is owning outcomes end-to-end, which involves identifying customer problems, shaping architecture, and building production systems that drive measurable impact and return on investment.
How is the team structured?
The role joins the initial team of Forward-Deployed Engineers, and the team operates with the speed and focus of a rapidly scaling startup.