InnoData

Technical Project Manager

• Own day-to-day WBS execution and schedule maintenance against the 24-week program schedule
• Draft weekly status reports for Delivery Owner review before AFS submission
• Manage AFS working-level coordination: technical POCs, dependency tracking, acceptance criteria follow-up
• Maintain risk and issue log; escalate decisions and blockers to the Delivery Owner
• Package and submit phase-milestone deliverables with explicit acceptance requests and deadline tracking
• Administer CVAT + SAM 2 toolchain in coordination with the Data & Annotation Engineer
• Facilitate standups, working sessions, and phase gate preparation
• Coordinate Technical Writer onboarding and documentation production in Phase E

Solutions Engineer

• Lead technical discovery with prospective and existing customers — foundation model labs, frontier AI teams, and large enterprises — to understand model objectives, gaps, and constraints.
• Design end-to-end solutions across the post-training stack: SFT data curation, preference data collection for RLHF/DPO, golden datasets, custom benchmarks, LLM-as-judge pipelines, human-in-the-loop evaluation, red teaming, and multimodal eval (text, image, audio, video, long-context).
• Architect engagements that combine Innodata’s platforms (GenAI Test & Evaluation Platform, Annotation Platform, GenAI Workbench) with our global SME workforce across 85+ languages and domains.
• Author technical proposals, SOWs, solution diagrams, and pricing models in partnership with sales, delivery, and finance.
• Run technical workshops, POCs, and pilot designs that de-risk larger programs and prove value quickly.
• Serve as the ongoing technical advisor during delivery, partnering with applied research scientists, AI/ML research engineers, language data scientists, and program managers to keep solutions aligned with the original intent.
• Feed customer signal back into Innodata’s R&D and product roadmap — what benchmarks customers actually want, where eval methodology is breaking, what new fine-tuning paradigms are gaining traction.
• Stay current on the state of the art in evals (e.g., dynamic and agentic benchmarks, capability vs. safety evals, long-context and tool-use evaluation) and post-training (SFT, RLHF, DPO, RLAIF, rejection sampling, distillation).
• Represent Innodata externally — at customer reviews, conferences, and in technical content.

Technical Solutions Architect, Evals & Fine-Tuning

• Own cross-pillar architectural integrity and interface coordination across the 24-week program
• Design and stand up the DataCard governance framework: schema design, distribution-statement methodology (DoD Dir. 5230.24), provenance lineage
• Deploy and configure ZenML multi-cloud orchestration stack per Innodata Layer 2 Multi-Cloud Architecture v3
• Design taxonomy engine and sequestration controls architecture
• Confirm NIST SP 800-53 Rev 5 control split between Innodata data-layer and AFS infrastructure-layer scope
• Lead platform component run books and per-DataCard documentation in Phase E
• Coordinate with AI Solutions Engineer on Kubernetes-adjacent data-layer DLP policy configuration
• Coordinate with Backend Engineer on Databricks write-back schema and DataCard write-back path

Solution Architect

Senior Buyer

• Build and manage a global network of suppliers including universities, libraries, wholesalers, distributors, used-book marketplaces, and academic publishers
• Develop and execute procurement strategies to source large volumes of academic books across multiple countries efficiently and cost-effectively
• Identify, evaluate, and onboard new sourcing partners — negotiating pricing, terms, volume commitments, and exclusivity agreements
• Monitor market trends in academic publishing, used-book pricing, and library acquisitions to stay ahead of supply opportunities
• Build relationships with key stakeholders across the academic ecosystem — university bookstores, library systems, remainder dealers, and secondary market platforms
• Oversee end-to-end international logistics including freight forwarding, customs clearance, import/export compliance, and last-mile delivery
• Manage relationships with freight carriers, customs brokers, and third-party logistics providers (3PLs) across multiple geographies
• Design and optimize warehousing and fulfillment operations to handle large-scale book inventory across multiple locations
• Ensure timely, cost-efficient movement of inventory while maintaining quality standards and minimizing damage or loss
• Build contingency and risk mitigation plans for supply chain disruptions, customs delays, and carrier issues
• Implement and oversee inventory management systems that track stock levels, ISBN data, condition grading, and valuation across the entire catalog
• Ensure accurate cataloging and metadata management — ISBN, title, edition, publisher, condition — for all acquired inventory
• Develop quality control processes for grading and processing incoming used book inventory
• Optimize inventory turnover, reduce dead stock, and ensure fulfillment accuracy
• Negotiate contracts and service agreements with suppliers, logistics providers, and distribution partners
• Build long-term strategic sourcing partnerships that provide reliable, scalable supply at competitive pricing
• Develop performance metrics and scorecards for vendor management and ongoing evaluation
• Identify opportunities to expand sourcing into new markets, geographies, and academic categories.

Senior Book Buyer

Analytics Engineer

• Collaborate with Innodata Client Partners to scope and deliver technical pilots and POCs, earning the “technical win” with new and existing robotics customers.
• Work closely with Engineering and Product to design and implement robotics data capture solutions (real-world, lab, and simulation), including sensors/rigs, teleop/UMI flows, and egocentric capture pipelines.
• Collaborate directly with customers’ robotics and ML teams to understand their architectures (VLMs, VLAs, ACTs, world models, policies), define data and evaluation requirements, design execution plans, and translate them into actionable Statements of Work.
• Design and run customized demos, playbooks, and experimental workflows that showcase robotics data capabilities and shorten the time from idea to production pilot.
• Act as the technical owner of the customer experience: unblock integrations, debug data/workflow issues, and advise on best practices for data quality, coverage, and evaluation.
• Collaborate closely with Robotics Project Leads to ensure clean handoff from pre-sales design to delivery, align on scope and success metrics, and support smooth execution of data collection programs.
• Bring structured, frontline feedback from customers back into Innodata’s product and roadmap discussions, helping shape a scalable Robotics & Physical AI data practice and platform.
• Represent Innodata at key industry events, conferences, and workshops, and contribute as a thought leader through talks, blog posts, and open artifacts that share learnings from our robotics data work.

Robotics Solutions Engineer – Robotics Data Collection, Physical AI

Robotics

• Translate the requirements of robotics foundation models, including vision-language-action models, world models, and manipulation and locomotion policies, into concrete data specifications covering modalities, action representations, sampling, annotation schemas, and evaluation criteria.
• Determine how Innodata's existing and incoming data should be structured, formatted, and enriched to maximize its value for training and evaluating robot foundation models.
• Guide the robotics data collection effort across capture modalities, including motion capture, egocentric, exocentric, teleoperation, multi-sensor, and synthetic generation, so that collected data maps cleanly to model needs.
• Run experiments that validate data design, including fine-tuning and evaluating foundation models on Innodata data to demonstrate that specific data decisions produce measurable model improvement.
• Develop evaluation and benchmarking methodology for robotics data and models, grounded in measurable criteria such as coverage, discriminative power, reliability, and actionability.
• Collaborate with the capture lab, annotation teams, and the synthetic data pipeline to turn data specifications into operational collection and labeling plans.
• Represent Innodata's data approach in technical conversations with research and robotics organizations.

Robotics – Physical AI Research Engineer

• Own end-to-end delivery of egocentric robotics data collection programs from kickoff through final hour acceptance, ensuring all client quality, diversity, and timeline targets are met
• Develop and maintain a standard collection playbook adaptable across clients with adjustable parameters — camera geometry, naturalism blend, mount type, regions — rather than rebuilt from scratch each engagement
• Build reusable delivery assets including SOPs, onboarding and training curricula, scorecards, rubrics, and tracking infrastructure shared across client programs
• Monitor daily project progress against hour targets and acceptance rates, identifying and resolving bottlenecks before they impact delivery
• Act on client QA feedback within 24 hours, relay actionable guidance to contributors, and drive measurable improvement across quality, diversity, and task adherence dimensions
• Collaborate closely with Robotics Solutions Engineers to ensure clean handoff from pre-sales design to delivery, aligning on scope, success metrics, and data collection execution plans
• Develop a working understanding of client robotics architectures — including VLMs, VLAs, ACTs, world models, and manipulation policies — to translate model requirements into concrete data collection specifications
• Translate client technical requirements (camera geometry, IMU specifications, naturalism targets, egocentric capture pipeline standards, file format requirements) into clear contributor guidance and internal delivery standards
• Partner with clients’ robotics and ML teams to understand evolving data needs and proactively adjust collection programs to ensure dataset utility for model training
• Maintain fluency in egocentric capture best practices, sensor/rig configurations, and common failure modes (e.g. robotic movement, poor framing, low task diversity) to engineer quality upstream
• Oversee a continuous contributor recruitment funnel capable of sourcing and onboarding 10,000+ participants at scale across US, LatAm, and developed Asia markets
• Partner with recruiting channels — crowd work agencies, BPO and workplace partnerships, universities, gig platforms, and referral networks — to maintain a reliable, diversified sourcing pipeline
• Design and enforce participant onboarding and training programs that front-load quality, addressing common failure modes before volume collection begins
• Monitor contributor-level metrics including acceptance rates, fraud rates, diversity scores, and hour contribution, taking corrective action where needed
• Serve as the primary operational point of contact for robotics clients during active collection engagements, maintaining strong relationships and positioning Innodata for contract expansion
• Provide regular project status updates to clients and internal leadership, including hour delivery progress, acceptance rates, fraud rates, and open issues
• Bring structured, frontline feedback from clients back into Innodata’s product and roadmap discussions, helping shape a scalable Robotics & Physical AI data practice
• Represent Innodata’s robotics data capabilities at key industry events and contribute to thought leadership through talks and published artifacts
• Coordinate device procurement, configuration, shipping, and recovery across collection regions, with a preference for company-owned, centrally managed iPhone fleets over participant-owned devices
• Standardize on client-defined hardware rigs — head mount, tilted-down geometry — and mitigate known comfort issues via mount fit, session length guidance, and participant training
• Build and manage a logistics function to procure, configure, track, refurbish, and redeploy devices across LatAm, Asia, and US markets
• Enforce fraud prevention protocols across the contributor base, ensuring confirmed fraudulent sessions are flagged, not compensated, and that fraud rates stay below client thresholds
• Drive a quality-first collection culture through training, real-time monitoring, and fast feedback relay — treating QA as an upstream engineering problem, not a downstream fix
• Where clients provide backend QA access, plug into the feedback loop to continuously refine training and delivery

Robotics Data Collection Manager

QA Engineer

• Design and own the inter-annotator agreement (IAA) methodology for the Phase 1 demonstration corpus — metric selection (Cohen's kappa, Fleiss, Krippendorff's alpha), sampling design, adjudication workflow, and agreement thresholds
• Define evaluation framework architecture: test and evaluation plans, IAA targets, drift detection gates, and model performance metrics per SOW Section 2.9
• Configure and operate sampling-based quality control across the self-service and white-glove annotation paths during Phase D corpus production
• Design and implement confidence-threshold escalation routing from automated annotation to senior-annotator adjudication
• Validate quality scoring and IAA computation within the Innodata data layer
• Support AI Solutions Engineer on evaluation design for SAM 2 and Frontier model API validation — define what 'good enough' looks like quantitatively
• Produce evaluation framework documentation for the Phase 1 NPP closeout package, including per-DataCard documentation with the SA

QA Lead

Financial Planning and Analysis

• Own, build, and continuously improve sophisticated driver-based financial models spanning the consolidated forecast, annual operating plan, and long-range plan, including scenario, sensitivity, and unit-economics analysis to support executive and Board decisions.
• Apply deep familiarity with services / delivery-based business economics — utilization, billable capacity, project margins, revenue recognition timing, and headcount-to-revenue linkage.
• Own defined FP&A processes end to end — from data through analysis to reporting — and drive standardization, automation, and repeatable close and forecast cycles rather than relying on ad hoc effort.
• Communicate clearly and credibly with senior leadership and executives, translating complex financial analysis into concise, decision-ready narratives and materials.
• Lead the monthly close partnership with Accounting, including variance analysis, revenue reconciliation, and management reporting.
• Prepare Board, investor, and executive materials with strict adherence to public-company disclosure discipline.
• Partner with operations and delivery leaders to translate operational metrics into financial outcomes.
• Support corporate development, including valuation frameworks, diligence support, and deal modeling.
• Mentor analysts and contribute to the maturation of the FP&A function.

Manager/Senior Manager, Financial Planning & Analysis

Artificial Intelligence

• Rating/assessing the performance of AI models or algorithms based on their output or behavior through a set of evaluative questions.
• Labeling elements of a piece of content rather than the content as a whole.
• Assigning predefined categories or labels to items.
• Evaluating the perceived quality and/or appropriateness of content
• Generating labels to advance understanding of a concept, trend etc.
• Creation of additional training data for machine learning models by applying transformations to the original data, such as modifying images (rotation, flipping, cropping), generating new text (paraphrasing, summarization), or altering audio/video signals (speed modification, pitch shifting) to reduce overfitting and increase dataset diversity.
• Reviewing data and identifying whether or not a product feature works as intended based on the project's guidelines.
• Labeling model outputs to identify if a piece of content is or isn't something. Examples: identify clickbait; identifying gaming videos; identifying branded content.
• Ordering or ranking items based on a set of preferences or criteria.
• Creating prompts or questions that will be used to generate responses from a language model or other AI system.
• Projects that evaluate the relevance of content based on a relevancy scale (1-3, 1-5, etc.).
• Generating responses to prompts or questions using a language model or other AI system.
• Rewriting existing text while preserving the original meaning, often to improve clarity or style and adherence to guidelines.
• Producing concise summaries of longer pieces of text or data.
• Converting spoken language or audio content into written text.
• Converting text or spoken language from one language to another.
• Gathering and compiling various forms of data to be used for training, evaluating, or fine-tuning the AI models. This may include text, images, videos, audio files, or other types of digital content.

Generative AI Specialist

• Rating/assessing the performance of AI models or algorithms based on their output or behavior through a set of evaluative questions.
• Labeling elements of a piece of content rather than the content as a whole.
• Assigning predefined categories or labels to items.
• Evaluating the perceived quality and/or appropriateness of content.
• Generating labels to advance understanding of a concept, trend etc.
• Creation of additional training data for machine learning models by applying transformations to the original data, such as modifying images (rotation, flipping, cropping), generating new text (paraphrasing, summarization), or altering audio/video signals (speed modification, pitch shifting) to reduce overfitting and increase dataset diversity.
• Reviewing data and identifying whether or not a product feature works as intended based on the project's guidelines.
• Labeling model outputs to identify if a piece of content is or isn't something. Examples: identify clickbait; identifying gaming videos; identifying branded content.
• Ordering or ranking items based on a set of preferences or criteria.
• Creating prompts or questions that will be used to generate responses from a language model or other AI system.
• Projects that evaluate the relevance of content based on a relevancy scale (1-3, 1-5, etc.).
• Generating responses to prompts or questions using a language model or other AI system.
• Rewriting existing text while preserving the original meaning, often to improve clarity or style and adherence to guidelines.
• Producing concise summaries of longer pieces of text or data.
• Converting spoken language or audio content into written text.
• Converting text or spoken language from one language to another.
• Gathering and compiling various forms of data to be used for training, evaluating, or fine-tuning the AI models. This may include text, images, videos, audio files, or other types of digital content.

Generative AI Associate – PhD

• Rating/assessing the performance of AI models or algorithms based on their output or behavior through a set of evaluative questions.
• Labeling elements of a piece of content rather than the content as a whole.
• Assigning predefined categories or labels to items.
• Evaluating the perceived quality and/or appropriateness of content.
• Generating labels to advance understanding of a concept, trend etc.
• Creation of additional training data for machine learning models by applying transformations to the original data.
• Reviewing data and identifying whether or not a product feature works as intended based on the project's guidelines.
• Labeling model outputs to identify if a piece of content is or isn't something.
• Ordering or ranking items based on a set of preferences or criteria.
• Creating prompts or questions that will be used to generate responses from a language model or other AI system.
• Projects that evaluate the relevance of content based on a relevancy scale.
• Generating responses to prompts or questions using a language model or other AI system.
• Rewriting existing text while preserving the original meaning.
• Producing concise summaries of longer pieces of text or data.
• Converting spoken language or audio content into written text.
• Converting text or spoken language from one language to another.
• Gathering and compiling various forms of data to be used for training, evaluating, or fine-tuning the AI models.