ABOUT THE ROLE

We are looking for a talented Sr. GenAI Engineer who sits at the intersection of knowledge engineering, agentic AI, and data intelligence. In this role you will design and operate AI agents that traverse, reason over, and enrich large-scale knowledge graphs — then extend that context dynamically using live data sources such as the web, enterprise APIs, and structured databases.

The ideal candidate is deeply comfortable with graph data models, LLM orchestration frameworks, and retrieval-augmented pipelines. Bonus points if you have experience working in trade-craft or intelligence-adjacent environments where provenance, precision, and adversarial robustness are non-negotiable.

KEY RESPONSIBILITIES

Knowledge Graph Engineering

• Design, build and maintain large-scale property graphs and RDF triplestores (Neo4j, Amazon Neptune, Stardog, or equivalent).
• Develop and govern ontologies, taxonomies, and entity-relationship schemas that reflect real-world domain semantics.
• Implement graph ingestion pipelines that extract, transform, and link entities from structured, semi-structured, and unstructured data.
• Optimise graph traversal queries (Cypher, SPARQL, Gremlin) for sub-second response at production scale.
• Train and deploy graph neural networks (GNNs) for node classification, link prediction, and subgraph retrieval - Maintain model retraining workflows triggered by graph drift or coverage degradation.

Agentic AI Systems

• Architect and implement autonomous agents that plan multi-step reasoning chains over knowledge graph data using LLMs (GPT-4o, Claude, Gemini, or open-source equivalents).
• Build graph-aware Retrieval-Augmented Generation (RAG) pipelines that blend structured graph context with unstructured document retrieval.
• Design tool-use and function-calling layers so agents can query live data sources — web search, REST/GraphQL APIs, relational databases — to extend or verify graph knowledge.
• Implement agent memory, reflection, and self-correction loops to improve reliability over multi-hop tasks.

Context Enrichment & Data Fusion

• Integrate web scraping, news feeds, and open-source intelligence (OSINT) sources to keep the knowledge graph current.
• Build entity resolution and deduplication components that merge data from heterogeneous sources into a consistent graph.
• Develop confidence-scoring and provenance-tracking mechanisms so downstream consumers understand the reliability of any piece of context.

MLOps & Production Readiness

• Package agents as scalable microservices; instruments with observability tooling (tracing, latency, token cost).
• Collaborate with platform engineers to deploy workloads on cloud-native infrastructure (AWS / GCP / Azure).
• Maintain evaluation harnesses that measure agent accuracy, hallucination rate, and graph coverage over time.

REQUIRED SKILLS & EXPERIENCE

• 5 + years of professional software engineering with strong Python (or Java / Kotlin) proficiency.
• Hands-on production experience with at least one major graph database — Neo4j, Amazon Neptune, TigerGraph, or comparable.
• Demonstrated knowledge of graph query languages like Cypher, SPARQL, or Gremlin — at production query complexity.
• Direct experience building LLM-powered agents or pipelines using frameworks such as LangChain, LangGraph, LlamaIndex, CrewAI, AutoGen, or Semantic Kernel.
• Solid understanding of RAG architectures: chunking strategies, vector stores (Pinecone, Weaviate, pgvector), hybrid retrieval, and re-ranking.
• Familiarity with prompt engineering, few-shot learning, and LLM evaluation techniques.
• Experience integrating external data sources via APIs, web scraping (Playwright / Scrapy), or streaming pipelines (Kafka / Kinesis).
• Working knowledge of containerisation (Docker, Kubernetes) and CI/CD pipelines.
• Familiarity with graph export formats - at least one GraphML, RDF/OWL, or JSON-LDExperience integrating GNN-derived features into vector stores or RAG pipelines

PREFERRED QUALIFICATIONS

• Advanced degree (MS / PhD) in Computer Science, Information Science, Computational Linguistics, or a related field.
• Experience in intelligence, defence, or trade-craft environments — working with OSINT, link analysis, entity disambiguation, or signals intelligence data.
• Understanding of access-control models for sensitive graph data (need-to-know, compartmentalisation, provenance labelling).
• Familiarity with knowledge representation standards like OWL, SHACL, RDF-star, JSON-LD, W3C PROV.
• Experience with fine-tuning or instruction-tuning open-source LLMs (Llama, Mistral, Falcon) for domain-specific tasks.
• Background in network-analysis algorithms: centrality, community detection, path-finding, anomaly detection on graphs.
• Contributions to open-source graph or GenAI projects; published research or technical blog presence.
• Active or adjudicatable security clearance (Secret or above) — strongly preferred for trade-craft assignments.

We are seeking a senior, quota-carrying Enterprise Sales Director to drive net-new enterprise revenue by helping Fortune 500 organizations hire and scale high-performing remote technical talent. This role is focused specifically on Turing’s Talent and Staffing offerings, with an emphasis on deploying remote contractors across AI, data, and software engineering functions.

This is a high-impact individual contributor role designed for a seasoned enterprise seller who thrives on hunting, closing, and expanding strategic accounts. This position does not include people management responsibilities, but it does require strong ownership, executive-level communication, and comfort selling into complex, multi-stakeholder environments.

The ideal candidate brings deep experience selling IT Services, Managed Services, or technical staffing solutions into large enterprises and has an established network of senior IT decision-makers.

Responsibilities

• Full ownership of enterprise revenue generation within assigned accounts and territory
• Net-new Fortune 500 logo acquisition
• Manage 3–6 month enterprise sales cycles and close multi-stakeholder talent engagements from discovery through contracting
• Executive relationship development with CIOs, CTOs, Heads of Engineering, Heads of Data/AI, and other technical leaders
• Run end-to-end enterprise sales process from prospecting through negotiation and contract execution
• Maintain accurate forecasting and disciplined pipeline management
• Develop strategic account penetration and expansion plans to grow a significant book of business over time

Qualifications

• 10+ years of enterprise B2B sales experience
• Bachelor's degree or similar experience in enterprise sales.
• Proven track record selling IT Services, Managed Services, Engineering Services, or technical staffing solutions
• Demonstrated success building and growing enterprise accounts to $3M+ in annualized revenue through talent/staffing solutions
• Established rolodex of enterprise Fortune 500 IT decision-makers
• Strong understanding of technical roles and comfort engaging engineering executives on hiring needs, team structures, and talent tradeoffs
• Strong executive presence and consultative selling skills
• Ability to navigate complex procurement, legal, and multi-stakeholder buying processes
• Self-starter who operates with urgency and ownership

Compensation: $320,000 to $360,000 OTE (base+commission) + equity

Role Overview

We are looking for Strategic Project Leads (SPLs) to drive large-scale AI data operations and lead high-impact programs for frontier AI labs.

This is a hands-on, people management + operations leadership role at the intersection of AI, data, and large-scale execution.

You will own end-to-end delivery of complex AI workflows (e.g., RLHF, SFT, model evaluation, benchmarking), manage large distributed teams, and ensure high-quality output under aggressive timelines.

This role is ideal for sharp, high-pedigree operators (Top tier MBA) who thrive in ambiguity, enjoy solving complex problems, and can scale both systems and teams.

What You’ll Do

1. End-to-End Delivery Ownership

• Own execution of large-scale AI data programs (multi-million dollar scope)
• Translate ambiguous client/research requirements into structured workflows
• Drive delivery across RLHF, SFT, coding annotation, and model evaluation pipelines
• Ensure outcomes across quality, speed, and cost

1. Operations & Systems Leadership

• Design and optimize end-to-end data pipelines
• Identify bottlenecks and implement improvements in:
• Throughput
• Quality
• Cost efficiency
• Build scalable systems including:
• Workflow design
• Incentive structures
• Review and QA mechanisms
• Drive continuous iteration and process improvement (“build → test → refine” loops)

1. People & Team Leadership (Core to Role)

• Lead and manage large distributed teams (100s to 1000+ contributors)
• Build and manage pods/team structures
• Drive performance through:
• Clear goal setting
• Quality benchmarks
• Feedback loops
• Hire, train, and mentor high-performing operators
• Act as a force multiplier for team productivity and output

1. Client & Stakeholder Management

• Act as the primary interface with AI researchers and enterprise clients
• Build strong, trust-based relationships with stakeholders
• Provide structured updates, insights, and recommendations
• Anticipate client needs and drive proactive problem solving

1. AI Model Evaluation & Benchmarking

• Work on model benchmarking initiatives (e.g., hill climbing benchmarks)
• Design prompts and evaluates outputs across models (GPT, Claude, Gemini, etc.)
• Analyze performance gaps and drive improvements
• Contribute to building evaluation frameworks and quality standards

1. Cross-Functional Collaboration

• Partner closely with R&D / FDL teams (e.g., Anshul’s team) on technical alignment
• Bridge the gap between research and operations
• Translate technical requirements into scalable execution plans

What We’re Looking For

• Experience: 4–10 years (MBB/Tier-1), high-growth startups, or strategy & operations/program management/Human Data management roles in high-intensity environments
• Education (Preferred): MBA from top US university or leading global management programs
• Problem Solving & Ownership: Strong first-principles thinking with the ability to break down ambiguous problems and build scalable solutions (0→1, 1→10)
• Operations Excellence: Proven experience managing complex workflows with a focus on throughput, quality, efficiency, and data-driven decision making
• People Leadership: Experience managing teams or pods; ability to drive performance, accountability, and team development
• Tech Literacy: Comfortable working with AI/ML workflows (LLMs, RLHF, evaluation); able to collaborate effectively with technical teams and understand data pipelines
• Coding Proficiency: Coding proficiency, preferably in Python, to perform data validation, write simple quality checks/scripts, and ensure output correctness in coding workflows
• Communication: Clear, structured communicator with strong client-facing and stakeholder management skills

What This Role is NOT

• Not a pure software engineering role
• Not a strategy-only or PPT-driven role
• Not a passive program management role

This is a builder + operator role with ownership and intensity

Key Expectations

• Operate in a fast-paced, high-ownership environment
• Manage time-sensitive, high-pressure deliverables
• Occasionally work across time zones / off-hours when required
• Maintain a very high bar for quality and execution

Comp- 150-230K Base 

About the role: 

Turing is looking for people with GenAI experience to join us in solving business problems for our Fortune 500 customers. You will be a key member of the Turing Intelligence delivery organization and part of  a GenAI project. You will be required to lead a team of other Turing engineers across different skill sets. In the past, the Turing GenAI delivery organization has implemented industry leading multi-agent LLM systems and LLM deployments for major enterprises. 

Required skills:

• 12+ years of professional experience in software engineers and building applications/systems
• 2+ years of hands-on experience in how LLMs work & Generative AI (LLM) techniques particularly multi-agent systems. 
• Expert proficiency in programming skills in Python, Langgraph and SQL is a must.
• Expert in architecting GenAI applications/systems using various frameworks & cloud services
• Expert proficiency in using AI tools like claude code, codex, cursor, windsurf and the likes. 
• Expert proficiency in AI observability & evaluation tools like Langsmith, Langfuse or similar
• Good proficiency in using various cloud services from Azure, GCP, or AWS for building the GenAI applications
• Experience in driving the engineering team toward a technical roadmap.
• Excellent communication skills to effectively collaborate with business SMEs

Roles & Responsibilities:

• Solutioning & Lead
• Build the technical roadmap given a business requirement and own the delivery of the same.
• Lead the engineering team toward a technical roadmap and ensure timely execution of the roadmap to achieve customer satisfaction. 
• Design robust multi-agent architectures including supervisor-router patterns with dynamic sub-agent routing and stopping conditions
• Mentoring and guidance: Provide technical leadership and knowledge-sharing to the engineering team, fostering best practices in machine learning and large language model development.
• Hands-on skills
• Develop LLM-based solutions: Lead the design, training, fine-tuning, and deployment of large language models, leveraging techniques like retrieval-augmented generation (RAG) and multi-agent based architectures.
• Build and maintain agent evaluation pipelines, including offline eval datasets, LLM-as-judge, and CI-integrated eval runs
• Codebase ownership: Build & maintain high-quality, efficient code in Python (using frameworks like LangChain/LangGraph) and SQL, focusing on reusable components, scalability, and performance best practices.
• Cloud integration: Deployment of GenAI applications on cloud platforms (Azure, GCP, or AWS), optimizing resource usage and ensuring robust CI/CD processes.
• Communication
• Actively follows the frontier and has differentiated, up-to-date views on model releases, agentic architectures, evaluation methods, tool-use and computer-use patterns, multimodal capability, reasoning/test-time compute trends, and the serious open questions in the field.
• Produce a structured, high-signal answer to an open-ended technical or strategic question — while modulating depth for a non-engineering executive audience.
• Cross-functional collaboration: Work closely with product owners, data scientists, and business SMEs to define project requirements, translate technical details, and deliver impactful AI products.

Salary: $220k - $300k base

Strategic Project Lead / Delivery Manager 

Data Annotation & AI Delivery

8+ Years of Experience

Position Overview

We are looking for a seasoned Delivery Manager (8+ years of experience) to lead large-scale data annotation and curation projects in support of cutting-edge LLM and multimodal AI research. You will oversee end-to-end delivery for projects with 100+ team members, ensure world-class data quality, and drive process and tooling innovation in collaboration with AI researchers and cross-functional teams.

This role is ideal for someone who combines deep technical expertise, proven leadership in managing large teams, and a passion for advancing AI/ML innovation. Given that our key customers operate in the data analytics, data engineering, and Business Intelligence (BI) space, the ideal candidate will bring hands-on leadership experience with platforms such as Databricks and Snowflake, enabling them to deeply understand customer domains, speak their language, and deliver annotation outcomes that are contextually relevant and technically sound.

Primary Responsibilities

1. Team Leadership & Development

• Lead, mentor, and grow large-scale teams of annotators and trainers.
• Drive performance management, career development, and targeted training.
• Foster a culture of excellence, ownership, and continuous learning.
• Design and implement motivation strategies (including gamification) to boost engagement and reward top performers.

1. Project Management & Coordination

• Define and manage project scope, objectives, timelines, and quality metrics.
• Align with cross-functional stakeholders on goals and deliverables.
• Proactively identify and resolve delivery risks, blockers, and dependencies.
• Provide clear and consistent reporting on progress, challenges, and solutions.

1. Data Quality & Integrity

• Own quality control and data integrity across the annotation lifecycle.
• Analyze datasets to identify trends, anomalies, and insights for improvement.
• Implement best practices for annotation, evaluation, and data curation.
• Share learnings and continuously improve quality processes.

1. Technical Collaboration & Innovation

• Partner with engineering and research teams to optimize internal tooling.
• Stay ahead of emerging trends in AI, ML, and multimodal processing.
• Champion innovations that reduce task completion times and improve efficiency.
• Maintain clear and scalable documentation for processes and training.

1. Customer & Domain Engagement

• Serve as a domain-knowledgeable point of contact for customers operating in data engineering, analytics, and BI environments.
• Leverage hands-on experience with platforms like Databricks and Snowflake to understand customer data workflows, terminology, and quality expectations.
• Translate customer-specific data engineering and BI contexts into clear, actionable annotation guidelines and quality benchmarks for delivery teams.
• Collaborate with customers to define annotation taxonomies and evaluation criteria that align with their data pipeline and analytics use cases.

Required Skills & Qualifications

• 10+ years of experience in engineering, program management, or delivery of large-scale projects.
• Proven ability to lead and scale large teams (100+ members) with measurable impact.
• Strong technical grounding in AI/ML, data annotation, and multimodal systems.
• Hands-on leadership experience in data analytics, data engineering, or BI domains, with working knowledge of platforms such as Databricks and/or Snowflake (architecture, pipelines, data modeling, and production workflows).
• Familiarity with the modern data stack including ETL/ELT pipelines, data warehouses, data lakes, and BI tools (Power BI, Tableau, Looker, or similar).
• Ability to translate complex data engineering and analytics concepts into annotation frameworks, quality standards, and training materials.
• Proficiency in Python (preferred) or Swift for data analysis and automation.
• Strong mathematical and analytical skills for data-driven problem solving.
• Experience in data curation, annotation pipelines, and quality control.
• Excellent communication and stakeholder management skills.
• Track record of driving high-quality outputs under tight deadlines.

Preferred Skills & Qualifications

• Advanced degree in Computer Science, Data Science, AI, or related fields.
• Experience applying gamification to workforce engagement.
• Familiarity with modern project management tools & methodologies.
• Exposure to state-of-the-art multimodal AI technologies.
• Prior experience managing annotation or data labeling projects for customers in the data engineering, analytics, or BI space.
• Familiarity with SQL at an advanced level and data orchestration tools such as Apache Airflow, dbt, or Prefect.
• Certifications in Databricks (e.g., Data Engineer Associate) or Snowflake (e.g., SnowPro Core) are a plus.

Overview

We are seeking a seasoned techno-functional leader to drive the development and execution of large-scale LLM training programs. This leader would partner with our clients (leading LLM labs) research teams to:

• Identify opportunities for building training datasets to improve model capabilities and performance
• Generate these datasets with high quality and speed
• Build automation tools and processes for scalability
• Deliver the datasets so that they are easily usable by our clients

Key Responsibilities

Operational Leadership & Performance Management

• Lead and scale global delivery teams of 100+, distributed across functions, regions, and levels (ICs, leads, and managers)
• Implement performance management systems that go beyond managerial reporting using data-driven metrics, tools, and products to assess productivity, quality, and output consistency
• Build strong operational structures that allow for transparency, accountability, and early detection of underperformance
• Partner with cross-functional leads to optimize workflows and improve internal tool adoption for delivery efficiency

Data Quality & Scripting-Driven Automation

• Own the quality, accuracy, and scalability of data generated for LLM training
• Move beyond manual QA layers by leveraging Python scripting, APIs, and automation frameworks to measure, validate, and improve dataset integrity
• Design and oversee tools or scripts for data validation, annotation accuracy checks, and pipeline consistency
• Ensure datasets adhere to compliance standards (PII, GDPR, HIPAA) and can be programmatically tested for usability and quality

LLM Training & Evaluation

• Lead generation and delivery of high-quality, scalable datasets focused on SFT, RLHF, reasoning, and agentic workflows
• Oversee the entire data lifecycle from client intake and annotation workflow design to delivery
• Partner with product, research, and engineering teams to implement evaluation metrics (e.g., win rate, inter-annotator agreement, and pairwise preference scoring)

Client Partnership & Communication

• Serve as the primary point of contact for enterprise AI clients; manage expectations, delivery timelines, and escalations
• Build relationships with engineering and research stakeholders by delivering consistently high-quality data
• Communicate effectively across technical and non-technical audiences; provide transparency through structured updates and quality reporting

Team Development & Tooling

• Recruit, mentor, and coach cross-functional leaders (Eng, Data, Ops, and Program Management)
• Drive adoption and improvement of internal tools (e.g., task management systems, quality dashboards)
• Champion continuous improvement across data quality, tools, and delivery processes

Required Qualifications

• 10+ years of experience leading large-scale technical delivery organizations, ideally across AI, ML, or data operations
• Bachelor's degree in Engineering, Computer Science, or equivalent technical discipline
• Demonstrated ability to act as a strategic business partner with our clients, researchers, and engineers at leading LLM labs
• Proven success in building and scaling multi-level high performance teams, with distributed global operations
• Experience managing managers
• Skip-level performance management
• Hands-on technical fluency: ability to write and review data validation scripts
• Demonstrated experience managing dataset generation or annotation for machine learning model evaluation and/or training 
• Familiarity with ML tools and data workflows (e.g., HuggingFace, LangChain, Weights & Biases, Databricks)

Preferred Qualifications

• Experience evaluating large language model performance and/or improving model performance via fine-tuning
• Strong understanding of data quality frameworks, including automation, toolings and manual processes 
• Experience in AI data annotation, model evaluation, and fine-tuning platforms
• Strong communication and storytelling skills with executive stakeholders

Location SF Bay Area (Hybrid)

Compensation: $255,000 to $325,000 OTE + Equity

About the Role

We’re seeking a highly skilled and motivated Forward Deployed Engineer (FDE) to work at the cutting edge of Generative AI deployments. In this role, you’ll partner directly with customers to design, build, and deploy intelligent applications using Python, Langchain/LangGraph, and large language models. You’ll bridge engineering excellence with customer empathy to solve high-impact real-world problems.

This is a hands-on engineering role embedded within customer projects—ideal for engineers who enjoy ownership, love solving hard problems, and thrive in dynamic, technical environments.

Key Responsibilities

• Lead the end-to-end deployment of GenAI applications for customers—from discovery to delivery.
  
  
• Architect and implement robust, scalable solutions using Python, Langchain/LangGraph, and LLM frameworks.
  
  
• Act as a trusted technical advisor to customers, understanding their needs and crafting tailored AI solutions.
  
  
• Collaborate closely with product, ML, and engineering teams to influence roadmap and core platform capabilities.
  
  
• Write clean, maintainable code and build reusable modules to streamline future deployments.
  
  
• Operate across cloud platforms (AWS, Azure, GCP) to ensure secure, performant infrastructure.
  
  
• Continuously improve deployment tools, pipelines, and methodologies to reduce time-to-value.
  
  

Required Qualifications

• 5–8+ years of experience in software engineering or solutions engineering, ideally in a customer-facing capacity.
  
  
• Proven expertise in Python, Langchain, LangGraph, and SQL.
  
  
• Deep experience with engineering architecture, including APIs, microservices, and event-driven systems.
  
  
• Demonstrated success in designing and deploying GenAI applications into production environments.
  
  
• Strong proficiency with cloud services such as AWS, GCP, and/or Azure.
  
  
• Excellent communication skills, with the ability to translate technical complexity to customer-facing narratives.
  
  
• Comfortable working autonomously and managing multiple deployment tracks in parallel.
  
  

Preferred Qualifications

• Familiarity with CI/CD, infrastructure-as-code (Terraform, Pulumi), and container orchestration (Docker, Kubernetes).
  
  
• Background in LLM fine-tuning, retrieval-augmented generation (RAG), or AI/ML operations.
  
  
• Previous experience in a startup, consulting, or fast-paced customer-obsessed environment.
  
  

Education

• Bachelor's, Master's, or Ph.D. in Computer Science, Engineering, or a related technical field.

Salary Range: 180-230k+ 

Location: United States (Bay Area preferred - Hybrid) 

Experience: 6–10+ Years

Type: Individual Contributor 

About the Role

We are looking for an exceptional Senior Technical Recruiter to join our Talent team. This is a full-cycle, individual contributor role responsible for driving end-to-end recruitment for technical positions across the organization, with a focus on AI, ML, and agentic engineering talent.

The ideal candidate is a strategic operator — someone who recruits with the end in mind, builds trusted relationships with executive stakeholders, and closes competitive offers without flinching. You bring deep sourcing expertise, strong market instincts, and a data-driven approach to every search. You have a track record at companies people recognize, long tenures that reflect real ownership, and the startup experience to thrive in a high-velocity, high-ambiguity environment.

This is not a requisition-processing role. We are looking for someone who treats recruiting as a craft and AI as a competitive advantage.

What You'll Do

• Own and manage the full recruitment lifecycle for technical roles, with particular depth in AI, ML, agentic systems, and software engineering
• Partner directly with executive and senior hiring managers to run structured intake meetings, define hiring strategy, and align on success criteria
• Build proactive, multi-channel sourcing strategies using LinkedIn Recruiter, GitHub, Boolean search, talent mapping, referral networks, and AI-powered sourcing tools
• Screen and evaluate candidates for both technical capability and cultural alignment
• Manage the interview process end-to-end, ensuring a consistent and high-quality candidate experience
• Drive stakeholder alignment and influence hiring decisions at the executive level with data, market intelligence, and clear point-of-view
• Lead offer negotiations and candidate closing conversations, including managing competing offers, counter-offer scenarios, and complex compensation structures
• Provide ongoing market intelligence: compensation benchmarks, talent mapping, competitor hiring trends, and AI talent landscape shifts
• Track and report on pipeline metrics, conversion rates, and time-to-fill; maintain rigorous ATS hygiene (Greenhouse preferred)
• Champion AI-forward recruiting practices — actively evaluate and adopt tools that improve sourcing quality, pipeline efficiency, and candidate assessment
• Continuously improve hiring processes for speed, quality, and equity

What We're Looking For

Experience

• 6–10+ years of full-cycle technical recruiting experience, with at least 3 years in a senior or lead individual contributor capacity
• Demonstrated success hiring for Engineering, AI/ML, Agentic Systems, Data, or Security roles in competitive markets
• Proven experience influencing executive-level stakeholders and partnering with senior hiring managers as a strategic advisor
• Meaningful tenure at previous employers reflecting real ownership and investment in outcomes, not frequent role-hopping
• Startup or high-growth experience preferred; comfort with ambiguity, shifting priorities, and building process where little exists
• US-based hiring experience required; global hiring exposure a strong plus

Core Competencies

Technical / Hard Skills

• End-to-end technical recruiting with a specialization in AI/ML or agentic engineering talent
• Advanced sourcing expertise: Boolean search, LinkedIn Recruiter, GitHub, talent mapping, and AI-assisted sourcing tools
• Offer negotiation and candidate closing, including competing offer management and counter-offer inoculation
• Data-driven recruiting: pipeline metrics, conversion tracking, capacity planning, and reporting to leadership
• Market intelligence: compensation benchmarking, talent landscape analysis, competitor insights
• ATS proficiency (Greenhouse preferred)

Soft Skills

• Executive presence and the ability to influence senior stakeholders with data and clear recommendations
• Strategic problem-solving — you diagnose root causes, not just symptoms
• Strong ownership mindset: you treat every open role as your own business problem to solve
• High resilience and accountability; you close what you open
• Comfort operating at startup speed with limited support infrastructure
• Intellectual curiosity about AI and its implications for the talent landscape

Ideal Background

We are especially interested in candidates who have built their careers at:

• AI-first companies or AI research organizations
• High-growth technology startups (Series B through IPO)
• SaaS or product-led technology companies
• Enterprise technology firms with rigorous technical hiring standards

The strongest candidates will have a clear arc — serious companies, meaningful tenures, progressively complex roles — and will be able to speak credibly about the AI talent market, not just navigate a requisition queue.

Compensation: $140,000–$175,000+ base

Actual compensation will be determined based on experience, skills, and qualifications. Turing is an equal opportunity employer.