Your Role:

You will leverage your experience and creativity to become a trusted resource for People Leaders and Team Members. You view navigating ambiguity as an opportunity to build out and optimize scalable processes. 

As we are in build mode, you will be able to spot plenty of business opportunities and more importantly play a pivotal role in being a part of the business solution. Aside from core HRBP responsibilities, each HRBP will have special areas of responsibility. 

Our Approach: 

We aim to provide efficient, customer-centric, and dignified experiences to all of our stakeholders. We are not here to be “HR for HR's sake”, rather we aim to empower the success of our team members and therefore the business. We achieve these results by developing actionable analyses based on our expertise and all available data. 

Core Responsibilities: 

• Act as a Senior HR Business Partner, balancing immediate business needs with long-term people and organizational strategy.
• Lead and own HRBP-driven programs including performance management, succession planning, and compensation cycles.
• Provide strategic counsel and data-driven insights to senior leaders.
• Independently manage sensitive employee relations cases, escalating thoughtfully when required.
• Operate with broad scope and autonomy, taking on evolving responsibilities in support of company growth and change.

Areas of Focus: 

Strategic Partnership & Advisory

• Partner with senior business leaders to develop and implement HR strategies that directly support business goals, performance, and long-term workforce planning.  

Talent & Organizational Development

• Lead talent review processes, leadership development, and career pathing initiatives to strengthen leadership pipelines and organizational capability.  

Organizational Design & Change Management

• Drive complex change management and organizational design initiatives, ensuring smooth transitions, employee adoption, and business continuity.  

Performance & Culture

• Partner with leaders to shape high-performance cultures through performance management guidance, coaching, and targeted interventions.  

Employee Relations & Complex Case Guidance

• Provide counsel on employee relations matters and support resolution strategies for sensitive issues.  

People Analytics & Insights

• Analyze workforce and HR metrics to inform people decisions, identify trends, and recommend proactive solutions that enhance organizational outcomes.  

Qualifications: 

• Education: Bachelor’s degree in Human Resources, Business, or related field (Master’s preferred).  
• Experience: Typically 7+ years of progressive HR experience with significant experience in strategic HR and business partnering roles.  
• Leadership & Influence: Proven ability to work with senior leaders, influence decision-making, and translate business needs into people strategies.  
• Analytical Skills: Strong data-driven mindset with experience interpreting HR metrics and translating insights into action.

Bonus Points 

• Foundational knowledge of corporate finance, economics, and/or statistics.

What we do

We create RL environments to evaluate and improve our customers' models on complex, long-range, multi-step workflows across high-GDP-value domains such as Finance, Sales, Retail, Developer Tools, Collaboration, Customer Experience. 

The environments vary depending on the model capability being evaluated / improved, a few examples of environment types are listed here: 

1. Environments for Software Engineering / coding agents 
2. UI-Environments for Computer-Use/Browser-Use agents 
3. MCP-based Environments for general function-calling agents across various enterprise and consumer applications.

The Role

We are looking for a Frontier Data Lead – RL to own the end-to-end lifecycle of RL environment projects, spanning environment design, task generation, reward/verifier design, quality, and delivery to frontier AI labs and enterprise clients. 

This is a hands-on technical leadership role where you influence revenue directly – you will be mapped to one or more AI labs and build RL environments specific to their needs. You will lead teams of engineers, subject matter experts (e.g. Finance expert, if you’re building an RL environment for investment banking workflows), researchers, and data ops teammates to achieve this.

What You’ll Do 

• End-to-End Ownership: Lead RL Environment projects end-to-end for one or more clients, ensuring the environment you and your team create matches the client’s spec, surpasses quality expectations, and is delivered on time. 
• Data Quality: Ensure the RL environments you produce, the data that goes into those environments, and the the data generated from them (e.g. agent trajectories and reward scores) meet frontier standards for realism, difficulty, diversity. 
• Team building and enablement: Work with your Ops counterparts to build the team of full-stack engineers, back-end engineers, domain experts, QAs, data creators, reviewers, and others you’ll need to deliver the environment on time. You’ll interview, hire, onboard, train, retain talent for your team 
• Process Leadership: Set the process that each of the above team members follows to generate environment code, database schemas, seed data, tasks, and verifiers; set up quality rubrics, automated validation scripts, and human-in-the-loop review processes for every aspect of the environment and data for the environment.
• Customer Interaction: Own customer relationships for your RL Environment project(s), and act as the primary point of contact for leading AI labs, providing regular updates, asking for feedback, and identifying opportunities to grow project scope and revenue. 
• Sales & Solutioning: participate in client solutioning conversations alongside our sales teammates; understand the needs of researchers at AI labs, translate those needs into environment goals 
• Evals & Post-training: Demonstrate proof of value for your environments by running inhouse RL fine tuning experiments to measure model performance lifts on agent trajectories; or by producing eval reports of frontier models on your environment and tasks

Who We’re Looking For

• RL & Post-training experience: familiarity with RL fine tuning, verifier/reward design, and/or environment design 
• Engineering Management experience: have led teams of engineers in the past, including interviewing/hiring them and setting up QA processes.
• Systems thinking + Database/API design: ability to ‘simulate’ the data schema and API interface of a consumer or business application 
• Hands-on technical capability: willing to write code along with the team you’re managing; Python and SQL experience preferred 
• Operational leadership: Proven ability to manage complex data pipelines, multi-stakeholder delivery, and concurrent high-stakes projects. 
• Cross-functional communicator: ability to communicate clearly with researchers at frontier AI labs, subject matter experts for various domains, and diverse teams. 
• Background in Computer Science, Machine Learning, or related technical field preferred.

Why Turing 

•  Work directly with the world’s leading AI labs and enterprises at the cutting edge of RL environment design and post-training. 
• Real impact: your environments will be used to evaluate and train frontier models on GDP-moving tasks across real-world domains 
• Talent-dense team, where you'll find high autonomy, rapid iteration, and rapid learning curve

Compensation: $250,000 to $350,000 OTE + Equity

What we do

Turing builds large-scale datasets and reinforcement learning (RL) environments that power post-training for the world’s leading AI labs and enterprises. We create RL environments to evaluate and improve our customers' models on complex, long-range, multi-step workflows across high-GDP-value domains such as Finance, Sales, Retail, Developer Tools, Collaboration, Customer Experience. 

The environments vary depending on the model capability being evaluated / improved, a few examples of environment types are listed here: 

1. Environments for Software Engineering / coding agents 
2. UI-Environments for Computer-Use/Browser-Use agents 
3. MCP-based Environments for general function-calling agents across various enterprise and consumer applications.

The Role

We are looking for a Principal Research Engineer – RL to own the end-to-end lifecycle of RL environment projects, spanning environment design, task generation, reward/verifier design, quality, and delivery to frontier AI labs and enterprise clients. 

This is a hands-on technical leadership role where you influence revenue directly – you will be mapped to one or more AI labs and build RL environments specific to their needs. You will lead teams of engineers, subject matter experts (e.g. Finance expert, if you’re building an RL environment for investment banking workflows), researchers, and data ops teammates to achieve this.

What You’ll Do 

• End-to-End Ownership: Lead RL Environment projects end-to-end for one or more clients, ensuring the environment you and your team create matches the client’s spec, surpasses quality expectations, and is delivered on time. 
• Data Quality: Ensure the RL environments you produce, the data that goes into those environments, and the the data generated from them (e.g. agent trajectories and reward scores) meet frontier standards for realism, difficulty, diversity. 
• Team building and enablement: Work with your Ops counterparts to build the team of full-stack engineers, back-end engineers, domain experts, QAs, data creators, reviewers, and others you’ll need to deliver the environment on time. You’ll interview, hire, onboard, train, retain talent for your team 
• Process Leadership: Set the process that each of the above team members follows to generate environment code, database schemas, seed data, tasks, and verifiers; set up quality rubrics, automated validation scripts, and human-in-the-loop review processes for every aspect of the environment and data for the environment.
• Customer Interaction: Own customer relationships for your RL Environment project(s), and act as the primary point of contact for leading AI labs, providing regular updates, asking for feedback, and identifying opportunities to grow project scope and revenue. 
• Sales & Solutioning: participate in client solutioning conversations alongside our sales teammates; understand the needs of researchers at AI labs, translate those needs into environment goals 
• Evals & Post-training: Demonstrate proof of value for your environments by running inhouse RL fine tuning experiments to measure model performance lifts on agent trajectories; or by producing eval reports of frontier models on your environment and tasks

Who We’re Looking For

• RL & Post-training experience: familiarity with RL fine tuning, verifier/reward design, and/or environment design 
• Engineering Management experience: have led teams of engineers in the past, including interviewing/hiring them and setting up QA processes.
• Systems thinking + Database/API design: ability to ‘simulate’ the data schema and API interface of a consumer or business application 
• Hands-on technical capability: willing to write code along with the team you’re managing; Python and SQL experience preferred 
• Operational leadership: Proven ability to manage complex data pipelines, multi-stakeholder delivery, and concurrent high-stakes projects. 
• Cross-functional communicator: ability to communicate clearly with researchers at frontier AI labs, subject matter experts for various domains, and diverse teams. 
• Background in Computer Science, Machine Learning, or related technical field preferred.

Why Turing 

•  Work directly with the world’s leading AI labs and enterprises at the cutting edge of RL environment design and post-training. 
• Real impact: your environments will be used to evaluate and train frontier models on GDP-moving tasks across real-world domains 
• Talent-dense team, where you'll find high autonomy, rapid iteration, and rapid learning curve

Compensation: $250,000 to $350,000 OTE + Equity

The Role

Turing builds large-scale datasets and reinforcement learning (RL) environments that power post-training for the world’s leading AI labs and enterprises. We create RL environments to evaluate and improve our customers' models on complex, long-range, multi-step workflows across high-GDP-value domains such as Finance, Sales, Retail, Developer Tools, Collaboration, Customer Experience. 

The environments vary depending on the model capability being evaluated / improved, a few examples of environment types are listed here: 

1. Environments for Software Engineering / coding agents 
2. UI-Environments for Computer-Use/Browser-Use agents 
3. MCP-based Environments for general function-calling agents across various enterprise and consumer applications

The Staff Research Engineer – Code will own end-to-end the creation of datasets, RL environments, and evals for frontier AI labs in the domain of coding agents and software engineering. This is a hands-on technical leadership role where you influence revenue directly – you will be mapped to one or more AI labs and interface directly with researchers / engineers at those labs to understand their needs and build data offerings to address those needs. To achieve this, you will build and manage teams of software engineers, researchers, QAs, and contractors/data-annotators from Turing’s talent pool of 4M+ developers.  

You’ll be responsible for delivering projects at frontier quality and scale—owning data quality, throughput, and timely delivery. You’ll define and manage data pipelines, validation workflows, and review processes to ensure datasets meet the highest standards for realism, correctness, and diversity. You’ll also develop automations, synthetic data generation systems, and internal tools to scale production efficiently. 

In short, you’ll run your project like a startup within Turing, owning both the technical architecture and the operational execution required to produce best-in-class datasets/environments/evals to make the world’s best coding agents and models even better at real-world coding tasks across the software development lifecycle.

What you’ll do

1. End-to-End Ownership: Data Quality, Process Design, and Team Building

• Lead the creation of datasets, rl environments, and evals focused on Coding Agents / Software Engineering for one or more AI lab customers.
• Ensure that everything you ship to clients meets frontier standards for realism, correctness, diversity, and difficulty.
• Set up quality rubrics, automated validation scripts, and human review processes for every stage of data generation.
• Build and lead cross-functional teams of software engineers, researchers, QAs, and data creators drawn from Turing’s 4M+ developer network.
• Interview, onboard, train, and mentor team members to ensure consistent output quality and technical excellence.

2. Collaborate with Researchers at Frontier Labs

• Act as the primary technical point of contact for your customer projects, interfacing directly with researchers and engineers at frontier AI labs to understand their coding agent roadmap and model data needs, to gather feedback, and to co-define success criteria for your projects.
• Provide regular progress updates, surface insights from model evaluations, and incorporate client feedback to improve future iterations.

3. Drive Research, Sales Enablement, and Industry Thought Leadership

• Fine-tune models in-house on Turing-generated datasets or Turing-rl-environment generated trajectories to determine model improvement as a proof of data quality
• Proactively build benchmarks and run evals on frontier models and coding agents to identify strengths and weaknesses on SWE tasks, and leverage these insights to inform product roadmap
• Equip customer-facing teams with the Evaluation reports, sample datasets, and trainings to enable them to communicate your data offerings to customers most effectively
• Publish research papers and technical posts on Turing’s data products, innovations in our synthetic data generation / automation pipelines, evaluations of frontier agents and models, and Turing’s model fine-tuning results on our datasets.

4. Build Tools and Infrastructure

• Oversee development of internal tools that accelerate data generation and verification (e.g., automated data scraping pipelines, unit test generators, repo sandboxing).
• Design dashboards and APIs for customers to run model evals, view performance reports, and integrate Turing data directly into their post-training pipelines.

What we’re looking for

• Post-training experience on SWE tasks or experience building coding agents: We expect that you have a deep understanding of data ingredients and design principles that lead to measurable coding model improvements, either from fine-tuning models to improve SWE capabilities or building your own coding agents to improve upon SWE capabilities of the base model.
• Engineering Management experience: have led teams of engineers in the past, including interviewing/hiring them and setting up QA processes.Hands-on technical capability: Fluency in Python and proficiency in one or more major languages (C++, Java, Go, Rust, or JS).
• Operational leadership: Proven ability to manage complex data pipelines, multi-stakeholder delivery, and concurrent high-stakes projects.
• Cross-functional communicator: ability to communicate clearly with researchers at frontier AI labs, subject matter experts for various domains, and diverse teams.
• Background in Computer Science, Machine Learning, or related technical field preferred.

Why Turing

• Work directly with the world’s leading AI labs and enterprises at the cutting edge of post-training and RL environment design.
• Real impact (path to AGI): your datasets and environments will directly influence the trajectory toward Artificial General Intelligence and, ultimately, Superintelligence. Coding is the core reasoning substrate of intelligence—advancing models’ ability to understand, design, and write code is effectively advancing their capacity for logic, planning, and abstract thought.
• Real Impact (GDP): automating software engineering unlocks one of the largest productivity frontiers in history. The software engineering market represents trillions in global GDP, and every percentage gain in automation translates to profound efficiency and innovation benefits across all industries.
• Talent-dense team, where you'll find high autonomy, rapid iteration, and an exceptional learning curve.

Compensation: $250,000 to $350,000 OTE + Equity

The Role

We create RL environments to evaluate and improve our customers' models on complex, long-range, multi-step workflows across high-GDP-value domains such as Finance, Sales, Retail, Developer Tools, Collaboration, Customer Experience. 

The environments vary depending on the model capability being evaluated / improved, a few examples of environment types are listed here: 

1. Environments for Software Engineering / coding agents 
2. UI-Environments for Computer-Use/Browser-Use agents 
3. MCP-based Environments for general function-calling agents across various enterprise and consumer applications

The Frontier Data Lead – Code will own end-to-end the creation of datasets, RL environments, and evals for frontier AI labs in the domain of coding agents and software engineering. This is a hands-on technical leadership role where you influence revenue directly – you will be mapped to one or more AI labs and interface directly with researchers / engineers at those labs to understand their needs and build data offerings to address those needs. To achieve this, you will build and manage teams of software engineers, researchers, QAs, and contractors/data-annotators from Turing’s talent pool of 4M+ developers.  

You’ll be responsible for delivering projects at frontier quality and scale—owning data quality, throughput, and timely delivery. You’ll define and manage data pipelines, validation workflows, and review processes to ensure datasets meet the highest standards for realism, correctness, and diversity. You’ll also develop automations, synthetic data generation systems, and internal tools to scale production efficiently. 

In short, you’ll run your project like a startup within Turing, owning both the technical architecture and the operational execution required to produce best-in-class datasets/environments/evals to make the world’s best coding agents and models even better at real-world coding tasks across the software development lifecycle.

What you’ll do

1. End-to-End Ownership: Data Quality, Process Design, and Team Building

• Lead the creation of datasets, rl environments, and evals focused on Coding Agents / Software Engineering for one or more AI lab customers.
• Ensure that everything you ship to clients meets frontier standards for realism, correctness, diversity, and difficulty.
• Set up quality rubrics, automated validation scripts, and human review processes for every stage of data generation.
• Build and lead cross-functional teams of software engineers, researchers, QAs, and data creators drawn from Turing’s 4M+ developer network.
• Interview, onboard, train, and mentor team members to ensure consistent output quality and technical excellence.

2. Collaborate with Researchers at Frontier Labs

• Act as the primary technical point of contact for your customer projects, interfacing directly with researchers and engineers at frontier AI labs to understand their coding agent roadmap and model data needs, to gather feedback, and to co-define success criteria for your projects.
• Provide regular progress updates, surface insights from model evaluations, and incorporate client feedback to improve future iterations.

3. Drive Research, Sales Enablement, and Industry Thought Leadership

• Fine-tune models in-house on Turing-generated datasets or Turing-rl-environment generated trajectories to determine model improvement as a proof of data quality
• Proactively build benchmarks and run evals on frontier models and coding agents to identify strengths and weaknesses on SWE tasks, and leverage these insights to inform product roadmap
• Equip customer-facing teams with the Evaluation reports, sample datasets, and trainings to enable them to communicate your data offerings to customers most effectively
• Publish research papers and technical posts on Turing’s data products, innovations in our synthetic data generation / automation pipelines, evaluations of frontier agents and models, and Turing’s model fine-tuning results on our datasets.

4. Build Tools and Infrastructure

• Oversee development of internal tools that accelerate data generation and verification (e.g., automated data scraping pipelines, unit test generators, repo sandboxing).
• Design dashboards and APIs for customers to run model evals, view performance reports, and integrate Turing data directly into their post-training pipelines.

What we’re looking for

• Post-training experience on SWE tasks or experience building coding agents: We expect that you have a deep understanding of data ingredients and design principles that lead to measurable coding model improvements, either from fine-tuning models to improve SWE capabilities or building your own coding agents to improve upon SWE capabilities of the base model.
• Engineering Management experience: have led teams of engineers in the past, including interviewing/hiring them and setting up QA processes.Hands-on technical capability: Fluency in Python and proficiency in one or more major languages (C++, Java, Go, Rust, or JS).
• Operational leadership: Proven ability to manage complex data pipelines, multi-stakeholder delivery, and concurrent high-stakes projects.
• Cross-functional communicator: ability to communicate clearly with researchers at frontier AI labs, subject matter experts for various domains, and diverse teams.
• Background in Computer Science, Machine Learning, or related technical field preferred.

Why Turing

• Work directly with the world’s leading AI labs and enterprises at the cutting edge of post-training and RL environment design.
• Real impact (path to AGI): your datasets and environments will directly influence the trajectory toward Artificial General Intelligence and, ultimately, Superintelligence. Coding is the core reasoning substrate of intelligence—advancing models’ ability to understand, design, and write code is effectively advancing their capacity for logic, planning, and abstract thought.
• Real Impact (GDP): automating software engineering unlocks one of the largest productivity frontiers in history. The software engineering market represents trillions in global GDP, and every percentage gain in automation translates to profound efficiency and innovation benefits across all industries.
• Talent-dense team, where you'll find high autonomy, rapid iteration, and an exceptional learning curve.

Compensation: $250,000 to $350,000 OTE + Equity

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

About the Role

We are looking for an experienced Marketplace/Data Annotation Recruiter to manage the end-to-end recruitment delivery for customer-side non-technical roles at Turing. You will be responsible for facilitating sourcing candidates, talent planning, capacity management, and internal supply optimization, while activating external sourcing channels where necessary. The ideal candidate has experience in client hiring setups, is highly organized, thrives in dynamic environments, and excels at stakeholder management.

Key Responsibilities

• Act as the single point of contact between the Internal Delivery and Talent Fulfillment teams for assigned projects.
• Manage end-to-end fulfillment for multiple client requirements concurrently (typically 3–4 at a time).
• Conduct talent pool assessments and identify supply-demand gaps.
• Activate external sourcing channels (via partnerships or talent acquisition teams) when internal supply is insufficient.
• Oversee resource planning and capacity management, ensuring timely ramp-ups for customer-side roles.
• Collaborate with Delivery Leads to understand project requirements and talent expectations.
• Ensure data accuracy and visibility across systems — maintaining reports on hiring pipelines, fulfillment progress, and performance metrics.
• Track and report fulfillment metrics, including fill rates, turnaround times, and candidate quality.
• Partner with recruiting and sourcing teams to improve process efficiency and hiring velocity.
• Deliver a strong stakeholder experience, maintaining proactive communication and alignment on timelines and expectations.
• Continuously look for opportunities to improve internal talent fulfillment processes and introduce best practices.
  
  

Qualifications & Experience

• 5–10 years of experience in talent operations, recruitment delivery, or resource planning within IT services, outsourcing, or global staffing environments.
• Proven experience in client-side hiring setups and resource & capacity management.
• Experience handling multiple simultaneous delivery pipelines and managing cross-functional stakeholders.
• Working knowledge of LinkedIn Recruiter (good to have) and Google tools (Sheets, Docs, Drive) for tracking and reporting.
• Data analysis and reporting skills — able to extract insights, identify trends, and drive data-backed decisions.
• Excellent communication, organization, and stakeholder management skills.
• Demonstrated ability to operate independently in a remote, fast-paced, and evolving environment.
• Experience working across global time zones and in cross-cultural teams.
  
  

Nice to Have

• Experience in non-technical recruitment delivery or managing customer-side talent fulfillment.
• Exposure to capacity planning tools or reporting dashboards.
• Prior experience in startups or high-growth organizations with minimal structure.
• Understanding of internal mobility, workforce planning, or project-based staffing models.

Soft Skills

• Strong stakeholder communication and collaboration skills.
• Analytical mindset with attention to detail.
• Adaptability — thrives in ambiguity and rapidly changing environments.
• Proactive problem solver with a focus on ownership over hierarchy.
• Comfortable handling multiple priorities and working under tight timelines.
  
  

Ideal Candidate Profile

The ideal candidate is a hands-on operator who thrives at the intersection of talent fulfillment, data-driven decision-making, and stakeholder management. They should be proactive, outcome-oriented, and capable of maintaining operational excellence in a remote-first, high-speed environment.

Position Overview

We are seeking a Strategic Project Lead to strengthen Turing’s India delivery layer for content, creative, and multimodal AI workflows. This is a new, high-visibility role built to support content-heavy enterprise engagements and will serve as a key node in Turing’s global delivery network. This role sits at the intersection of creative production and AI systems — ideal for someone who understands how high-quality visual content is created and can translate that into scalable AI workflows.

You will own programs end-to-end — spanning client partnership, delivery systems, and creative quality — while working closely with internal teams and external partners to build production-grade datasets and pipelines. The ideal candidate brings a strong background in marketing, media, or content operations and has the delivery instincts to own programs end-to-end. 

Primary Responsibilities

1. Delivery Ownership & Client Partnership

• Own P&L and commercial outcomes for assigned content, Image, Video, and marketing AI engagements, with full accountability for scope, quality, and client satisfaction
• Define engagement strategy and delivery architecture in partnership with account leads — anticipating risks, building mitigation plans, and making the right call when trade-offs arise
• Build and deepen strategic relationships with key enterprise accounts, positioning Turing as an indispensable long-term partner for Creative AI (Image, Video & Content) workflows
• Identify and shape expansion opportunities within existing accounts, translating evolving client needs into structured, high-value programs

2. Delivery Systems & Scalable Execution

• Architect delivery frameworks that set the standard for quality and consistency across content, media, and marketing AI programs — building systems, not just processes
• Define metrics and leading indicators to proactively manage quality, speed, and cost
• Build reusable playbooks and engagement templates that compound value across accounts and reduce time-to-execution as programs scale
• Scale and structure global delivery teams, ensuring clear ownership and fast decision-making

3. Cross-Functional Influence & Strategic Alignment

• Influence cross-functional prioritization — working with US account leads, engineering, and operations to align resources and execution behind what matters most for client outcomes
• Serve as the strategic connective tissue between India delivery capacity and global account growth, ensuring both sides move with coherence and speed
• Shape Turing’s content and media service offering by bringing ground-level delivery and client insights into internal roadmap and capability-building conversations

4. Technical & Domain Leadership (Content / Marketing / Multimodal)

• Own and define quality standards for image, video, and content generation workflows.  
• Lead initiatives across image/video creation, editing, and prompt design, ensuring outputs meet real-world creative and production standards
• Work closely with internal teams and external agencies to direct, produce, and scale high-quality visual datasets
• Translate advances in GenAI and multimodal models into practical workflows for content generation, evaluation, and optimization
• Build playbooks for prompting, creative direction, and visual quality evaluation that scale across clients and use cases 

Requirements

High Aptitude Problem Solver

• 5–8 years of experience in delivery management, program management, or operations — with a strong background in marketing, media, or content workflows
• Natural ability to work on any kind of problem: comfortable dealing with uncertainty, navigating with 80% correct answers, and pushing through ambiguity
• Able to define problem structure, break it into manageable steps, and show progress swiftly
• Strategic problem solver with a first-principles approach and long-term impact mindset

Leadership & Operational Excellence

• Demonstrated success managing cross-functional teams with direct delivery accountability
• Track record of delivering complex, multi-stakeholder programs on time and within budget constraints
• Experience with agile methodologies, sprint planning, and continuous improvement processes
• Strong analytical skills with ability to derive actionable insights from large datasets and operational metrics
• Prior experience handling and driving high-growth OKRs

Communication & Business Acumen

• Excellent communication skills for client-facing roles, including presentation and negotiation capabilities
• Business judgment for making trade-offs between quality, speed, and cost based on client requirements
• Cross-cultural collaboration experience, comfortable working across global teams and time zones

Others

• Comfortable working across global teams and time zones
• Location : Based out of India
• Engineering or Management degree from a top-tier institution (IIT, IIM, ISB, or equivalent) is preferred; consulting/startup experience strongly preferred
• Domain background in marketing technology, media production, content operations, or creative services
• Exposure to AI training data, content moderation, or annotation-adjacent workflows
• Knowledge of multimodal AI technologies and their application to content and media use cases

Role Overview

We are seeking a FinOps / Expense Analyst to be the finance partner to Accounting and the business for end‑to‑end purchasing and operating expense processes. You will own day‑to‑day operations in expense management, support variance analysis and expense forecasting, and build crisp reporting packages for leadership. You will improve policy adherence, reduce “shadow spend,” and raise the signal‑to‑noise ratio in our opex so leaders can make faster, better decisions.

Key Responsibilities

• Serve as the Finance point of contact for Zip (intake/PO), Concur (T&E), and Ironclad (contracts); ensure requests flow efficiently with proper approvals and documentation.
  
  
• Partner with Accounting on month‑end close for opex: accruals, reclasses, coding hygiene, and actuals vs. forecast reconciliations.
  
  
• Build and maintain variance reporting (month‑over‑month, quarter‑to‑date, YoY) with clear drivers and recommended actions.
  
  
• Support the FP&A Expense Lead on forecasting and budget refreshes; maintain driver‑based models at cost‑center and department levels.
  
  
• Develop and deliver reporting packages and executive dashboards covering opex trends, run‑rate analyses, policy exceptions, and savings opportunities.
  
  
• Stand up spend analytics and KPI frameworks (e.g., PO cycle time, policy compliance, non‑PO spend %, T&E per head, vendor concentration) and drive remediation.
  
  
• Coordinate with Procurement/Legal on vendor diligence, contract terms, renewals, and consolidation opportunities.
  
  
• Partner with Business Systems to enhance tooling, integrations, and AI‑assisted automation (document parsing, anomaly detection, policy checks).
  
  

Qualifications

• Experience: 3–6+ years in FP&A/FinOps/Accounting with hands‑on ownership of purchasing and expense workflows.
  
  
• Tools: SQL and BI tooling experience required; strong Excel/Google Sheets; NetSuite (or similar ERP) and Salesforce exposure preferred.
• Operating rigor: Detail‑oriented, organized, and deadline‑reliable; comfortable building processes that scale.
  
  
• Business partnering: Effective at working across Accounting, Legal, RevOps, and budget owners; diplomatic but firm on policy.
  
  
• Analytics: Strength in variance analysis, forecasting, and KPI design; able to translate data into action.
  
  
• Mindset: Proactive problem‑solver who seeks automation and drives outcomes.
  
  

Why Join Turing

• Play a foundational role in shaping how financial strategy supports a category‑defining company.
  
  
• Remote‑first culture with autonomy, flexibility, and high standards.
  
  
• A seat at the table in a company redefining what’s possible with applied AI.
  
  
• Competitive compensation with meaningful equity.
  
  

Department: Field Engineering — Pre-Sales (Founding)

Level: Senior (Staff level considered for exceptional candidates)

Domain: enterprise knowledge work (EKW)

Location: Strong preference for SF Bay Area but will consider Seattle and NYC.

Reports to: CRO (until VP, Field Engineering is hired)

Compensation: OTE $260–320K (Senior) or $325–400K (Staff) · 75/25 base/variable split · Equity

The Role

You will be the first technical partner to Turing's Research Partners selling and demoing custom and off-the-shelf human expert datasets into the frontier AI labs in the enterprise knowledge work domain. Every major lab is racing to push the frontier on multi-step reasoning over enterprise data, tool use, long-horizon task completion, and evaluation that reflects real work. They buy datasets, benchmarks, graders, and expert human expertise from Turing to train, post-train, and evaluate those capabilities. Your job is to convert our technical depth into won revenue.

This is a founding Field Engineering role. The playbook, the demo library, the qualification bar, and the handoff to Production Engineering do not yet exist — you will build them.

What You'll Do

1) Technical discovery — lead the technical track on every qualified EKW opportunity

• Partner with Research Partners to run the technical conversation with lab researchers and engineers.
• Understand what agentic capability the lab is trying to unlock, what "good" looks like, and what evaluations a post-training team would actually trust.
• Qualify opportunities against a bar you help define: scope, feasibility, strategic fit.

2) Solution architecture — translate capability goals into scoped Turing deliverables

• Map research goals to Turing's offering shapes: agentic trajectories, rubric-graded reasoning tasks, tool-use evaluations, and domain-specialist-built datasets.
• Author technical proposals that frontier lab research leads accept and the Production Engineering team can execute without a rewrite.

3) Prototyping and demo-building — prove the approach before contract

• Build reference agent loops, sample multi-step evaluations, and graded trajectories that demonstrate quality before contract signature.
• The demo has to run. Expect to write real code.

4) POC ownership — take paid pilots from kick-off to scale-up decision

• Design a measurement plan the lab's research team will actually read and act on.
• Define success criteria, own the cadence, convert POC to production contract.

5) R&D interface — channel GTM-to-R&D asks for Enterprise Knowledge Workflow opportunities

• Pre-digest technical asks before routing to R&D. Shield research time from ad hoc calendaring.
• Maintain a collaboration cadence that R&D teams trust.

6) Playbook building — codify what works so future hires scale faster than you did

• Document discovery scripts, qualification criteria, demo artifacts, and objection-handling patterns for EKW opportunities.
• Own the EKW section of the Field Engineering knowledge base.

Who We're Looking For

• 5+ years in applied AI, data engineering, or ML engineering, with meaningful work on agentic systems, RAG, tool use, or enterprise-knowledge LLM applications.
• Strong Python fluency and production experience with LLM orchestration frameworks (LangGraph, LlamaIndex, DSPy, or equivalents).
• Experience designing evaluations for multi-step reasoning or agentic systems — rubric design, trajectory grading, measurement beyond single-turn accuracy.
• Exposure to complex enterprise workflows (financial services, life sciences, legal, or similar) and the data and permission realities inside them.
• A high written communication bar: you can produce a scoping document that a frontier lab research lead accepts without a rewrite.
• Commercial instinct: you want to be in customer meetings, you can read a room, and you are willing to be measured on revenue.

Strong pluses

• Prior time at a frontier AI lab, an AI startup building agentic products, or an enterprise AI team shipping to production.
• Experience with agentic or reasoning benchmarks (e.g., GAIA, τ-bench, or equivalents).
• Background in pre-sales, solutions architecture, or technical consulting.

What success looks like

• 30 days: first FE-led POC signed; enterprise knowledge work domain discovery playbook v1 published; three demo artifacts in the library.
• 60 days: win rate on EKW opportunities you cover is materially above the non-covered baseline; qualification bar codified.
• 180 days: a second Pre-Sales AI Solutions Engineer in the EKW domain hired behind you, ramping off your playbook.

Why Turing

• Work directly with the world's leading AI labs at the cutting edge of post-training, evaluation, and agentic AI research.
• Real impact on the path to AGI: the datasets, evaluations, and playbooks you build will directly influence frontier model development.
• Founding-team leverage. You will set the standards, not inherit them.
• Direct-to-research customers. You will spend your time talking to the people building AGI, not to procurement.

How to apply

Send a resume or CV and a short note on a technical artifact you built — ideally something customer-facing, evaluation-adjacent, or that demonstrates how you think about technical scoping. We read every submission.

Department: Field Engineering — Pre-Sales (Founding)

Level: Senior (Staff level considered for exceptional candidates)

Domain: Software Engineering (SWE)

Location: Strong preference for SF Bay Area but will consider Seattle and NYC.

Reports to: CRO (until VP, Field Engineering is hired)

Compensation: OTE $260–320K (Senior) or $325–400K (Staff) · 75/25 base/variable split · Equity

The Role

You will be the first technical partner to Turing's Research Partners selling and demoing custom and off-the-shelf human expert datasets into the frontier AI labs in the software engineering domain. Every major lab is racing to push the frontier on code generation, agentic software engineering, and SWE evaluation. They buy datasets, benchmarks, graders, and expert human expertise from Turing to train, post-train, and evaluate those capabilities. Your job is to convert our technical depth into won revenue.

This is a Field Engineering founding role. The playbook, the demo library, the qualification bar, and the handoff to Production Engineering do not yet exist — you will build them.

What You'll Do

1) Technical discovery — lead the technical conversation on every qualified SWE opportunity

• Partner with Research Partners to run the technical track with AI researchers and research leads.
• Understand what they're training, what they're evaluating, where their pipeline breaks, and what a Turing-built artifact looks like in practice.
• Qualify opportunities against a bar you help define: scope, feasibility, strategic fit.

2) Solution architecture — translate lab needs into scoped Turing deliverables

• Map capability goals to Turing's offering shapes: custom human expert data, off-the-shelf datasets, and managed talent.
• Author technical proposals that AI researchers accept and the Production Engineering team can execute without a rewrite.

3) Prototyping and demo-building — prove the approach before contract

• Build sample eval tasks, reference dataset slices, graded trajectories, and working agentic scaffolds.
• Expect to write real code, not mock-ups. The demo has to run.

4) POC ownership — take paid pilots from kick-off to scale-up decision

• Design the measurement plan, define success criteria, own the cadence.
• The outcome you are measured on: POC converts to production contract.

5) R&D interface — be the pre-sales channel between GTM and R&D for SWE

• Pre-digest technical asks before routing to R&D. Shield research time from ad hoc calendaring.
• Maintain a predictable collaboration cadence that R&D teams trust.

6) Playbook building — codify what works so future hires scale faster than you did

• Document discovery scripts, qualification criteria, demo artifacts, and objection-handling patterns.
• Own the SWE section of the Field Engineering knowledge base.

Who We're Looking For

• 5+ years in software or ML engineering, with meaningful production experience on code-generation, code-understanding, or developer-tooling systems.
• Hands-on fluency in Python and modern LLM tooling; comfort reading and writing across at least one other major language (TypeScript, Go, Rust, or Java).
• Experience designing or working with evaluations for code models — benchmarks, rubric design, grader reliability, eval construction.
• Experience with large codebases, agentic SWE systems, or developer-facing AI products.
• A high written communication bar: you can produce a scoping document that a frontier lab engineer accepts without a rewrite.
• Commercial instinct: you want to be in customer meetings, you can read a room, and you are willing to be measured on revenue.

Strong pluses

• Prior time at a frontier AI lab, AI infrastructure company, or developer-tools company shipping to AI customers.
• Experience with SWE benchmark construction (e.g., SWE-bench, LiveCodeBench, or equivalents).
• Background in pre-sales, solutions architecture, or technical consulting.

What success looks like

• 30 days: first FE-led POC signed; software engineering domain discovery playbook v1 published; three demo artifacts in the library.
• 60 days: win rate on SWE opportunities you cover is materially above the non-covered baseline; qualification bar codified; R&D and Field Engineering interface running on a predictable cadence.
• 180 days: a second Pre-Sales AI Solutions Engineer in the SWE domain hired behind you, ramping off your playbook. You spend less than 30% of your time as a solo contributor and more than 70% multiplying through the function.
  
  

Why Turing

• Work directly with the world's leading AI labs at the cutting edge of post-training, evaluation, and agentic AI research.
• Real impact on the path to AGI: the datasets, evaluations, and playbooks you build will directly influence frontier model development.
• Founding-team leverage. You will set the standards, not inherit them.
• Direct-to-research customers. You will spend your time talking to the people building AGI, not to procurement.

How to apply

Send a Resume or CV and a short note on a technical artifact you built — ideally something customer-facing, evaluation-adjacent, or that demonstrates how you think about technical scoping. We read every submission.

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.

 Position Overview

We are seeking a Strategic Project Lead to own end-to-end delivery and growth for audio and multimodal AI initiatives. This is a client-facing, builder role at the forefront of AI — spanning audio, speech, language, and multimodal systems. The scope includes both commercial and technical ownership. You’ll identify opportunities for collaboration, define the structure and terms of each engagement, and ensure successful delivery across internal and external teams.

Success in this role requires the ability to navigate early-stage problem spaces, build trust with technical stakeholders, and consistently deliver outcomes in complex, evolving environments.

We are looking for a strategic thinker with strong Audio AI data annotation experience who can translate loosely defined technical objectives into executable programs and grow relationships by consistently delivering value in complex, evolving environments.

Primary Responsibilities

1. Delivery Ownership & Client Partnership

• Own P&L, delivery outcomes, and client satisfaction for audio and multimodal engagements
• Lead client conversations and solutioning, translating ambiguous needs into structured programs
• Build strong relationships with enterprise and frontier AI lab partners
• Identify and drive account expansion opportunities, shaping early-stage ideas into commercial engagements

2. Delivery Systems & Scalable Execution

• Design and run high-performance delivery systems across audio and multimodal workflows
• Define metrics and leading indicators to proactively manage quality, speed, and cost
• Build reusable playbooks and frameworks to accelerate program launches
• Scale and structure global delivery teams, ensuring clear ownership and fast decision-making

3. Cross-Functional Influence & Strategic Alignment

• Align product, engineering, and operations around client priorities
• Drive execution in ambiguous, fast-changing environments
• Feed client and delivery insights into internal roadmap and capability building

4. Technical & Domain Leadership (Audio / Multimodal)

• Own and evolve Turing’s audio and speech capability across ASR, TTS, conversational audio, and multimodal systems
• Build new audio offerings — including data generation, evaluation frameworks, alignment workflows, and emerging agentic/audio use cases
• Partner with ML/research teams to define benchmarks and evaluation methodologies
• Translate advances in LLMs, speech, and multimodal AI into scalable, client-ready solutions

Requirements

Core Experience

• Demonstrated experience in delivery management, program management, or technical operations — with exposure to AI/ML or data workflows
• Domain background and experience in audio/speech AI, ASR/TTS, NLP, or multimodal data annotation
• Familiarity with frontier AI lab workflows: model evaluation, RLHF, alignment, benchmarking. Knowledge of the latest multimodal AI technologies and how they can improve annotation pipelines

Problem Solving & Ownership

• Strong first-principles problem-solving ability; comfortable operating with ambiguity
• Proven ability to own and scale complex programs end-to-end
• High ownership mindset with bias for action

Communication & Execution

• Strong communication skills; able to engage technical and business stakeholders
• Ability to make trade-offs across quality, speed, and cost
• Strong analytical skills with experience using data to drive decisions

Other

• Comfortable working across global teams and time zones
• Location : Based out of US
• Salary: $200k - $250k 

About Turing:

Based in Palo Alto, California, Turing is one of the world's fastest-growing AI companies accelerating the advancement and deployment of powerful AI systems. Turing helps customers in two ways: working with the world’s leading AI labs to advance frontier model capabilities in thinking, reasoning, coding, agentic behavior , multimodality, multilingualism, STEM and frontier knowledge; and leveraging that expertise to build real-world AI systems that solve mission-critical priorities for Fortune 500 companies and government institutions. Turing has received numerous awards, including Forbes's "One of America's Best Startup Employers," #1 on The Information's annual list of "Most Promising B2B Companies," and Fast Company's annual list of the "World's Most Innovative Companies." Turing's leadership team includes AI technologists from industry giants Meta, Google, Microsoft, Apple, Amazon, Twitter, McKinsey, Bain, Stanford, Caltech, and MIT. For more information on Turing, visit www.turing.com. For information on upcoming Turing AGI Icons events, visit go.turing.com/agi-icons.

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.
• 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.

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+ 

About the role

Turing is building the next generation of enterprise AI, and this role sits at the center of that mission. As Head of AI Technology, you will define how some of the world’s largest companies harness emerging technologies to reimagine their operations, products, and workforce productivity.

This is technical focused role, on both strategic level and hands-on detailed level. You will be partner with the Head of AI Business, to set a technical vision for how AI systems can create lasting enterprise value, while also guiding teams through the complexity of turning that vision into real-world applications. The role requires the judgment to engage credibly with senior executives, the technical depth to drive architectural decisions, and the leadership to scale teams working at the frontier of applied AI.

You will thrive here if you are a hand-on AI technologist with your opinionated vision on what the future of AI enterprise applications would be. You also need to navigate ambiguity, inspire confidence in high-stakes client environments, and build organizations that balance speed with rigor. The impact of your work will be measured not only in successful deployments, but also in the common architecture you inspire, in the lasting trust you create with clients and in the new possibilities you open for how enterprises compete and grow in the era of AI.

Responsibilities

• Lead the technology direction and execution of Turing’s Generative AI practice, ensuring the execution of high-impact programs with enterprise clients.
• Interact with clients to ideate and design AI solutions to help solve enterprise problems
• Own the oversight for design, build and deployment of GenAI-powered applications, including agentic systems, copilots, reasoning workflows, and enterprise orchestration frameworks.
• Lead and manage Generative AI and Forward-Deployed Engineering teams, ensuring delivery excellence and clear growth opportunities while staying closely engaged in critical program and design decisions.
• Build trusted partnerships with senior enterprise stakeholders, shaping program objectives and aligning with their business transformation and productivity goals.
• Define and enforce rigorous engineering standards, methodologies, and quality management processes to ensure reliability, scalability, and security across enterprise GenAI applications.
• Translate engineering & delivery experience into repeatable playbooks and common product infrastructure that establish Turing as a trusted partner for deploying applied GenAI solutions at speed and at scale.
• Partner with commercial teams to scope, shape, and resource programs, ensuring engineering capability scales with client demand.
• Act as the accountable executive for engineering & delivery excellence in Generative AI applications, representing Turing with credibility and authority to senior external stakeholders.

Requirements

• 15+ years of experience in the engineering & delivery of technology services, with at least 5 years leading large-scale, client-facing engineering or applied AI practices.
• Prior accountability for delivery outcomes at enterprise scale — budgets, teams, and programs spanning multiple regions or business units.
• Deep and hands-on expertise in applied GenAI systems, e.g. agentic workflows, copilots, multimodal integration, orchestration frameworks, and enterprise deployment.
• Proven record of building and managing senior technical teams (including engineering managers, architects, and data leaders).
• Track record of working directly with C-level stakeholders in Fortune 500 or equivalent enterprises
• Experience shaping consulting-style or services-led programs where delivery quality and client satisfaction were the growth drivers.
• Strong business acumen and the ability to balance delivery excellence with commercial growth priorities.
• Demonstrated ability to scale operations while preserving technical depth and client intimacy.
• Exceptional communication skills and credibility in executive-level interactions with enterprise clients, internal leadership, and external partners.
• Background in Computer Science, Data Science or relevant field, with a strong preference for advanced degrees and/or recognized thought leadership in AI, demonstrated through patents, publications, industry awards, or equivalent credentials.
• You are currently based in or willing to relocate to New York City - this is a non-negotiable requirement

About the Role:

We’re looking for a highly skilled and motivated IT Automation Engineer who is passionate about solving operational challenges through automation. This role focuses entirely on designing, building, and maintaining automation workflows across IT systems and infrastructure using tools like Tines, Google Apps Script, and Python. You’ll help streamline IT processes — from user onboarding to system provisioning and offboarding — while ensuring scalability, accuracy, and efficiency across all IT operations.

Responsibilities:

• Design and develop end-to-end IT automation workflows using tools such as Tines, Google Apps Script, and Python.
• Automate employee onboarding, offboarding, and access provisioning processes across systems like Okta, Google Workspace, Slack, and Zoom.
• Build event-driven automation pipelines for system alerts, user lifecycle management, and IT workflows.
• Integrate APIs across multiple SaaS and infrastructure platforms to enable real-time automation and data synchronisation.
• Collaborate with Security and IT teams to implement Zero Trust and access automation using IdP integrations (Okta, Google Identity, Azure AD).
• Develop internal tools and scripts to reduce manual IT tasks and improve operational efficiency.
• Maintain automation logic, workflows, and configurations using version control and documentation standards.
• Implement monitoring and alerting for all automation pipelines to ensure reliability and performance.
• Research and adopt new automation frameworks, scripting techniques, and no-code/low-code tools to expand automation coverage.
• Create and maintain technical documentation, SOPs, and automation libraries for cross-team use.
• Automate the creation and maintenance of operational dashboards to provide real-time visibility into IT metrics, system health, and automation performance.
  
  

Requirements:

• Bachelor’s degree in Computer Science, Information Technology, or equivalent practical experience.
• 5+ years of experience in IT Automation, IT Engineering, or DevOps-style IT Operations.
• Strong programming and scripting background in Google Apps Script, Python, PowerShell, or JavaScript.
• Proven experience building workflows in automation platforms such as Tines, BetterCloud, Okta Workflows, Zapier, or Workato.
• Strong understanding of API integrations, webhooks, and event-based architecture.
• Experience automating identity and access management (IAM) operations via APIs (Okta, Google Identity, Azure AD).
• Knowledge of cloud-first environments (Google Workspace, AWS, or GCP).
• Familiarity with Zero Trust, SSO, and MFA automation principles.
• Excellent problem-solving, documentation, and analytical skills.
• A strong automation-first mindset — you continuously look for ways to eliminate manual processes and improve scalability.
  
  

The Role

We are looking for an elite engineer who builds like a hacker and communicates like a consultant. You will not just be building models in a notebook; you will be deployed to the front lines to build, architect, and ship end-to-end Agentic AI products directly for our most strategic customers.

This is a high-stakes role. You will face "tricky" customers who have vague requirements and high expectations. Your job is to translate their chaos into technical order, write production-grade code, and deploy autonomous agents that solve their actual business problems.

What You Will Do

• Build & Deploy (60%):

Design and ship full-stack AI applications. You own the pipeline from data ingestion to the LLM inference layer to the frontend interface.

Architect autonomous agentic workflows (using frameworks like LangChain, LangGraph, or custom implementations) that can reason, plan, and execute complex tasks.

Write robust, clean, and highly testable code (Python/Typescript). "It works on my machine" is not acceptable; it must work in the customer's restricted cloud environment.

Handle DevOps and MLOps constraints: Dockerizing agents, managing Kubernetes clusters, and optimizing inference latency.

• Customer Engineering (40%):

Serve as the technical face of the company for our most demanding accounts.

Navigate complex stakeholder environments. You will push back on unrealistic demands, clarify ambiguous requirements, and steer "tricky" customers toward technically feasible solutions without breaking rapport.

Translate technical constraints into business value for non-technical executives.

Generate high-quality data for our customers and ability to review the data generated by other experts. This is one of the most critical aspects of the role.

The "Must-Haves"

• Experience: 8–12 years of total engineering experience. You started as a strong Software Engineer and evolved into an AI specialist.
• Education: B.S./M.S. in Computer Science, Math, or Physics from a top-tier institution (IIT, MIT, Stanford, Harvard, Berkeley, CMU, or similar global top-ranking universities).
• Deep AI Fluency: You aren't just calling APIs. You understand RAG implementation nuances, vector database optimization, fine-tuning (LoRA/PEFT), and the architecture of agentic loops.
• Production Engineering: Experience with end-to-end deployment (AWS/GCP/Azure, CI/CD pipelines, Terraform, Docker).

The "Why You" (Soft Skills)

• Thick Skin & High EQ: You don't get flustered when a customer changes their mind for the third time in a week. You can hold your ground with a CTO and explain "why not" to a CEO.
• Extreme Attention to Detail: You catch the edge cases that others miss. You obsess over error handling, retry logic in agents, and data privacy.
• Grit: You enjoy the "messy" work of integrating legacy enterprise data just as much as prompting the latest model.

What will set you apart

• Public Code: A visible GitHub footprint is very helpful. We want to see your side projects, your contributions to open source, or your experiments with LLMs. Please include your GitHub link in your application.

Tech Stack

• Languages: Python (Expert).
• AI/Data (Expertise in all is not required): PyTorch, LangChain, LlamaIndex, Pinecone/Weaviate, OpenAI/Anthropic APIs, HuggingFace.
• Infra (Expertise in all is not required): Kubernetes, Terraform, AWS/GCP.

The Role

We are looking for an elite engineer who builds like a hacker and communicates like a consultant. You will not just be building models in a notebook; you will be deployed to the front lines to build, architect, and ship end-to-end Agentic AI products directly for our most strategic customers.

This is a high-stakes role. You will face "tricky" customers who have vague requirements and high expectations. Your job is to translate their chaos into technical order, write production-grade code, and deploy autonomous agents that solve their actual business problems.

What You Will Do

• Build & Deploy (60%):

Design and ship full-stack AI applications. You own the pipeline from data ingestion to the LLM inference layer to the frontend interface.

Architect autonomous agentic workflows (using frameworks like LangChain, LangGraph, or custom implementations) that can reason, plan, and execute complex tasks.

Write robust, clean, and highly testable code (Python/Typescript). "It works on my machine" is not acceptable; it must work in the customer's restricted cloud environment.

Handle DevOps and MLOps constraints: Dockerizing agents, managing Kubernetes clusters, and optimizing inference latency.

• Customer Engineering (40%):

Serve as the technical face of the company for our most demanding accounts.

Navigate complex stakeholder environments. You will push back on unrealistic demands, clarify ambiguous requirements, and steer "tricky" customers toward technically feasible solutions without breaking rapport.

Translate technical constraints into business value for non-technical executives.

Generate high-quality data for our customers and ability to review the data generated by other experts. This is one of the most critical aspects of the role.

The "Must-Haves"

• Experience: 8–12 years of total engineering experience. You started as a strong Software Engineer and evolved into an AI specialist.
• Education: B.S./M.S. in Computer Science, Math, or Physics from a top-tier institution (IIT, MIT, Stanford, Harvard, Berkeley, CMU, or similar global top-ranking universities).
• Deep AI Fluency: You aren't just calling APIs. You understand RAG implementation nuances, vector database optimization, fine-tuning (LoRA/PEFT), and the architecture of agentic loops.
• Production Engineering: Experience with end-to-end deployment (AWS/GCP/Azure, CI/CD pipelines, Terraform, Docker).

The "Why You" (Soft Skills)

• Thick Skin & High EQ: You don't get flustered when a customer changes their mind for the third time in a week. You can hold your ground with a CTO and explain "why not" to a CEO.
• Extreme Attention to Detail: You catch the edge cases that others miss. You obsess over error handling, retry logic in agents, and data privacy.
• Grit: You enjoy the "messy" work of integrating legacy enterprise data just as much as prompting the latest model.

What will set you apart

• Public Code: A visible GitHub footprint is very helpful. We want to see your side projects, your contributions to open source, or your experiments with LLMs. Please include your GitHub link in your application.

Tech Stack

• Languages: Python (Expert).
• AI/Data (Expertise in all is not required): PyTorch, LangChain, LlamaIndex, Pinecone/Weaviate, OpenAI/Anthropic APIs, HuggingFace.
• Infra (Expertise in all is not required): Kubernetes, Terraform, AWS/GCP.

Compensation

232k-260k base + variable + equity

About the role: 

Turing is looking for people with LLM experience to join us in solving business problems for our Fortune 500 customers. You will be a key member of the Turing GenAI delivery organization and part of a GenAI project. You will be required to work with 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, RAG systems, and Open Source LLM deployments for major enterprises. 

Required skills

• 8 + years of professional, hands-on engineering experience, including 4–6+ years specializing in building and deploying AI/ML models and systems.
• 1+ year of experience in developing Generative AI (LLM) applications using techniques like prompt engineering, RAG, and/or agents.
• Expert in architecting GenAI applications/systems using various frameworks & cloud services
• Good proficiency in using various cloud services from Azure, GCP, or AWS for building the GenAI applications
• Expert proficiency in programming skills in Python, Langchain/Langgraph and SQL is a must.
• Experience in driving the engineering team toward a technical roadmap.
• Excellent communication skills to effectively collaborate with business SMEs

Roles & Responsibilities

• 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. 
• Develop and optimize LLM-based solutions: Lead the design, training, fine-tuning, and deployment of large language models, leveraging techniques like prompt engineering, retrieval-augmented generation (RAG), and agent-based architectures.
• Codebase ownership: 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: Aide in deployment of GenAI applications on cloud platforms (Azure, GCP, or AWS), optimizing resource usage and ensuring robust CI/CD processes.
• 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.
• Mentoring and guidance: Provide technical leadership and knowledge-sharing to the engineering team, fostering best practices in machine learning and large language model development.
• Continuous innovation: Stay abreast of the latest advancements in LLM research and generative AI, proposing and experimenting with emerging techniques to drive ongoing improvements in model performance.

Education

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

About the role: 

Turing is looking for people with LLM experience to join us in solving business problems for our Fortune 500 customers. You will be a key member of the Turing GenAI delivery organization and part of  a GenAI project. You will be required to work with 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, RAG systems, and Open Source LLM deployments for major enterprises. 

Required skills

• 10–12+ years of professional, hands-on engineering experience, including 4–6+ years specializing in building and deploying AI/ML models and systems.
• 1+ years of experience in developing Generative AI (LLM) applications using techniques like prompt engineering, RAG, and/or agents.
• Expert in architecting GenAI applications/systems using various frameworks & cloud services
• Good proficiency in using various cloud services from Azure, GCP, or AWS for building the GenAI applications
• Expert proficiency in programming skills in Python, Langchain/Langgraph and SQL is a must.
• Experience in driving the engineering team toward a technical roadmap.
• Excellent communication skills to effectively collaborate with business SMEs

Roles & Responsibilities

• 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. 
• Develop and optimize LLM-based solutions: Lead the design, training, fine-tuning, and deployment of large language models, leveraging techniques like prompt engineering, retrieval-augmented generation (RAG), and agent-based architectures.
• Codebase ownership: 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: Aide in deployment of GenAI applications on cloud platforms (Azure, GCP, or AWS), optimizing resource usage and ensuring robust CI/CD processes.
• 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.
• Mentoring and guidance: Provide technical leadership and knowledge-sharing to the engineering team, fostering best practices in machine learning and large language model development.
• Continuous innovation: Stay abreast of the latest advancements in LLM research and generative AI, proposing and experimenting with emerging techniques to drive ongoing improvements in model performance.

Education

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

Salary Range:$200-245K Base + equity

This is a remote role and can be performed anywhere in Brazil.

The Role

We are looking for a Research Engineer to help deliver frontier-quality datasets, RL environments, and evaluations that improve state-of-the-art models for leading AI labs and enterprise clients.

This is a hands-on, research-facing technical leadership role. You will work directly with customer researchers/engineers to translate their model and post-training goals into concrete data and environment specifications, and drive the production of data that meets extremely high standards for correctness, realism, diversity, difficulty, and measurable model lift.

This role is designed for candidates with roughly 4 to 5 years of experience building and improving deep learning systems, especially where strong results depend on data quality, data curation, denoising, synthetic data generation, and rigorous evaluation. You’ll operate in one or more of the following capability areas:

• Coding and software engineering agents (repositories, unit tests, debugging, tool use, code reviews, long-horizon workflows)
• RL environments and verifier-based training (tasks, rewards/verifiers, trajectories, evaluation harnesses)
• Multimodal data and reasoning (text + images + documents + tables/charts; optional audio/video)
• STEM reasoning (math, physics, chemistry, bio, engineering – solution verification and error analysis)
• Modern embodied AI / VLM-driven agents (vision-language(-action) models, embodied task suites, tool/sensor/action abstractions, long-horizon interaction data)

What You’ll Do

1) Own data and environment quality from an AI researcher perspective

• Translate ambiguous research goals into clear data requirements: target skills, failure modes, difficulty calibration, coverage, and success metrics.
• Define what “good” looks like by creating detailed rubrics, counterexamples, and boundary cases (what to include vs. exclude).
• Perform deep, detail-oriented audits of produced data: spot subtle errors, reward hacking opportunities, leakage, ambiguity, inconsistent assumptions, and distribution shifts.
• Drive iterative improvements using evidence: error taxonomies, slice-based quality metrics, and model-behavior-informed refinements.

2) Design and build datasets and RL environments for your capability area(s)

• Contribute to or lead the design of:
• Task suites (single-step and long-horizon workflows)
• Ground-truth signals (verifiers, unit tests, structured checks, reward functions, automatic validators)
• Environment interfaces (APIs, tool schemas, state abstractions, database schemas, simulator-like dynamics)
• Depending on your mapped capability area(s), you may focus on:
• Coding / SWE agents: data reflecting real development work (codebase navigation, bug localization, patching, tests, code reviews, CI-like constraints, refactors, security fixes).
• Multimodality: tasks that test true multimodal reasoning (chart reading, document QA, UI understanding, diagram-based STEM reasoning, OCR-aware tasks).
• STEM: tasks with verifiable solutions (symbolic checks, reference solvers, numerical validation, step consistency, unit sanity).
• Modern embodied AI / VLM-driven agents: interaction data and environments for vision-language(-action) models (long-horizon tasks, instruction following grounded in visual context, robust action selection, safety/constraint adherence, adversarial state coverage).

3) Build robust validation, denoising, and synthetic data systems

• Implement automated validation and filtering to achieve frontier-grade signal-to-noise:
• Deduplication, decontamination, leakage checks
• Consistency checks (format, schema, invariants)
• Difficulty and diversity controls (coverage, novelty, long-tail)
• Develop synthetic data generation and augmentation pipelines where appropriate:
• Programmatic task generators
• Controlled perturbations to create hard negatives
• Scenario templating with diversity constraints
• Simulator-/tool-driven rollouts for trajectory data
• Create documentation and data cards: dataset intent, known limitations, recommended use, and evaluation linkage.

4) Use evaluations and training runs to prove impact

• Design and run evals that reflect the customer’s intended usage.
• Produce analysis that connects data to outcomes:
• Pre/post comparisons on targeted capability slices
• Error breakdowns and “why the model failed” narratives
• Ablations to identify which data attributes drive lift
• When needed, run in-house fine-tuning or RL-style experiments (or partner with research) to demonstrate that the data/environment improves model behavior in measurable ways.

5) Collaborate effectively with large production teams without being ops-heavy

• Work with cross-functional teams (engineers, researchers, QAs, domain SMEs, and large-scale data production groups) by providing:
• Clear specs, examples, and edge cases
• Fast feedback loops based on audits and quantitative signals
• Structured review processes focused on quality, not throughput alone
• You are expected to be highly engaged in reviewing and improving outputs from large annotation/creation efforts, but not primarily responsible for hiring, staffing, or people operations.

Who We’re Looking For

• 4–5 years of experience building or improving deep learning systems where data quality mattered materially (training, post-training, evals, or agentic systems).
• Strong intuition for the “data ingredients” that drive model improvements: what to collect, what to filter, what to synthesize, and how to measure.
• Ability to communicate clearly with researchers and engineers: turning research objectives into concrete specs, and turning messy outputs into actionable insights.
• Demonstrated ability to be extremely detail-oriented in diagnosing subtle data quality issues and failure modes.
• Solid programming ability with a bias for shipping:
• Python proficiency required
• Comfort with SQL/structured data workflows strongly preferred
• For coding-focused work: proficiency in one or more major languages (e.g., C++, Java, Go, Rust, JS/TS) is a plus
• Comfort designing quality systems:
• Rubrics, validation scripts, gold sets, sampling strategies
• Statistical checks and slice-based evaluation
• Human-in-the-loop review loops grounded in measurable criteria

Strong pluses

• RL or post-training experience (any of: RLHF/RLAIF, verifier training, reward modeling, RL fine-tuning, environment design).
• Experience with agentic evaluation (tool use, multi-step workflows, long-horizon tasks, trajectory analysis).
• Multimodal expertise (document understanding, charts, diagrams, OCR, UI/vision grounding; audio/video optional).
• STEM depth (math/physics/engineering) with an eye for verifiability and rigorous correctness.
• Modern embodied AI / VLM-driven agent experience (vision-language(-action) models, interaction datasets, embodied evals, long-horizon grounding, tool/sensor/action interfaces).
• Systems thinking: ability to “simulate” an application’s API/data schema and design tasks that realistically reflect real-world constraints and workflows.

Why Turing

• Work directly with the world’s leading AI labs and enterprises at the cutting edge of post-training and RL environment design.
• Real impact (path to AGI): your datasets and environments will directly influence the trajectory toward Artificial General Intelligence and, ultimately, Superintelligence.
• Real Impact (GDP): the systems you help build and evaluate target high-value workflows across industries, where even incremental improvements translate to significant productivity gains.
• Talent-dense team, where you'll find high autonomy, rapid iteration, and an exceptional learning curve.

Location: India (Mumbai/Pune/Hyderabad/Chennai/Bengaluru) - Remote
Experience: 4-8+ Years 
Type: Individual Contributor

About the Role

We are looking for an experienced and high-impact Senior Technical Recruiter to join our growing Talent team. This is a full-cycle, individual contributor role responsible for driving end-to-end recruitment for technical positions across the organization.

The ideal candidate brings strong sourcing depth, structured problem-solving, stakeholder management expertise, and proven closing ability. You will operate in a fast-paced, high-growth environment where ownership, urgency, and quality are equally important.

What You’ll Do

• Own and manage the full recruitment lifecycle for technical roles
  
  
• Conduct intake meetings and define hiring strategy with stakeholders
  
  
• Build proactive sourcing strategies using LinkedIn Recruiter, Naukri, GitHub, referrals, and niche platforms
  
  
• Screen and evaluate candidates for technical capability and culture fit
  
  
• Manage the interview process and ensure a seamless candidate experience
  
  
• Drive stakeholder alignment and influence hiring decisions
  
  
• Lead offer negotiations and candidate closing conversations
  
  
• Provide market intelligence (compensation benchmarks, talent mapping, competitor insights)
  
  
• Track pipeline metrics and maintain ATS hygiene (Greenhouse preferred)
  
  
• Continuously improve hiring processes for speed and quality
  
  

What We’re Looking For

Experience

• 4 - 8+ years of technical recruitment experience
  
  
• Proven experience managing full-cycle hiring independently
  
  
• Experience hiring for Engineering, AI/ML, Corporate roles, Data, Security, or other technical roles
  
  
• Experience in startup or high-growth environments preferred
  
  
• Exposure to India/US/global hiring is a strong plus
  
  

Core Competencies

Technical / Hard Skills

• End-to-end technical recruiting
  
  
• Advanced sourcing expertise (Boolean search, LinkedIn Recruiter, GitHub, talent mapping)
  
  
• Market intelligence and pipeline building
  
  
• Offer negotiation and candidate closing
  
  
• ATS experience (Greenhouse preferred)
  
  
• Data-driven recruitment and metric tracking
  
  

Soft Skills

• Strong stakeholder management and influencing skills
  
  
• Structured problem-solving ability
  
  
• Ability to operate at startup speed
  
  
• Resilience and ownership mindset
  
  
• Comfort with ambiguity and competing priorities
  
  
• High accountability and execution focus
  
  

Ideal Background

We encourage applications from recruiters who have worked in:

• High-growth startups
  
  
• SaaS or product-based companies
  
  
• Tech-first organizations
  
  
• IT services firms with strong technical hiring exposure
  
  

Ideal candidates have hired for Engineering, AI/ML, Corporate roles, Data, or Security roles, and are comfortable operating in high-velocity hiring environments.

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.

About the role: 

Turing is looking for people with LLM experience to join us in solving business problems for our Fortune 500 customers. You will be a key member of the Turing GenAI delivery organization and part of  a GenAI project. You will be required to work with 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, RAG systems, and Open Source LLM deployments for major enterprises. 

Roles & Responsibilities:

• Develop and optimize LLM-based solutions: Lead the design, training, fine-tuning, and deployment of large language models, leveraging techniques like prompt engineering, retrieval-augmented generation (RAG), and agent-based architectures.
• Codebase ownership: 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: Aide in deployment of GenAI applications on cloud platforms (Azure, GCP, or AWS), optimizing resource usage and ensuring robust CI/CD processes.
• 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.
• Mentoring and guidance: Provide technical leadership and knowledge-sharing to the engineering team, fostering best practices in machine learning and large language model development.
• Continuous innovation: Stay abreast of the latest advancements in LLM research and generative AI, proposing and experimenting with emerging techniques to drive ongoing improvements in model performance.

Required skills:

• 8+ years of professional experience in building Machine Learning models & systems
• 1+ years of hands-on experience in how LLMs work & Generative AI (LLM) techniques particularly prompt engineering, RAG, and agents.
• Experience in driving the engineering team toward a technical roadmap.
• Expert proficiency in programming skills in Python, Langchain/Langgraph and SQL is a must.
• Understanding of Cloud services, including Azure, GCP, or AWS
• Excellent communication skills to effectively collaborate with business SMEs

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.