InnoData

Sales Development Rep

• Conduct thorough research to identify and target potential customers within the ideal customer profile
• Utilize cold calling, email, social media, and LinkedIn to connect with prospects
• Develop personalized outreach strategies based on prospect needs and pain points
• Conduct conversations to understand prospect needs, business challenges, and buying authority
• Qualify leads based on company size, industry, job title, and budget
• Assess lead potential and prioritize opportunities
• Manage a pipeline of qualified leads by tracking progress and ensuring timely follow-up in Salesforce
• Schedule calls and meetings between qualified prospects and the Sales Director
• Collaborate with the Sales Director to ensure seamless handoff of qualified leads
• Establish rapport with potential customers and build trust
• Nurture leads by providing valuable information and addressing concerns
• Position as a trusted advisor throughout the sales cycle
• Monitor sales metrics and analyze performance data
• Use CRM tools to track outreach, qualification, and pipeline progression
• Provide regular updates to the sales leader on progress and insights

Sales Development Representative

Research Scientist

• Partner directly with customers and frontier labs building video understanding, video-language, and video-generation models
• Define how video data is designed, structured, and evaluated for video and multimodal models
• Translate model requirements into data specifications covering modalities, annotation schemas, sampling, and evaluation criteria
• Build evaluation methodologies for video understanding, including temporal grounding, long-context reasoning, multi-turn, cross-modal, and retrieval-and-grounding evaluation
• Build evaluation methodologies for video generation, including fidelity, temporal coherence, and physical plausibility
• Decide how video is structured, enriched, and sampled, including messy, domain-specific footage
• Run experiments and ablations connecting data choices to measurable model improvements
• Design adversarial and stumping evaluations to identify system failures and improve data
• Publish benchmarks, methodology, and papers
• Work with annotation teams, subject-matter experts, and synthetic-data pipelines on collection and labeling plans

Research Scientist, Video & Multimodal

• Define how Innodata designs, structures, and evaluates audio data for speech and audio models
• Translate speech and audio model requirements into data specifications covering modalities, transcription and annotation schemas, sampling, and evaluation criteria
• Build evaluation methodologies covering semantic accuracy, noise and accent robustness, code-switching, diarization error rate, speech naturalness and intelligibility, and streaming latency
• Structure, enrich, and sample audio across languages, accents, acoustic conditions, demographics, emotional and paralinguistic range, scripted and spontaneous speech, and speaker configurations
• Partner with transcription and linguistics leads to develop transcription specifications and quantify their impact on model results
• Partner with audio solutions and engineering teams to ensure collected audio meets model objectives
• Run fine-tuning and evaluation experiments with ablations linking data choices to measurable improvements
• Design adversarial and stumping evaluations to identify speech-system failures
• Publish benchmarks, methodology, and research papers
• Work with annotation teams, subject-matter experts, and synthetic- and augmented-audio pipelines to operationalize specifications
• Partner directly with customers and frontier labs building ASR, text-to-speech, speech-to-speech, conversational voice, diarization, and audio-language models

Research Scientist, Speech & Audio

• Define how Innodata designs, structures, and evaluates data for robot foundation models
• Translate robotics foundation-model requirements into data specifications, including modalities, action representations, sampling, annotation schemas, and evaluation criteria
• Decide what to capture in the real world versus generate in simulation
• Curate and weight training mixes across heterogeneous robot datasets, embodiments, action spaces, and sensor setups
• Guide data collection across motion-capture, egocentric, exocentric, teleoperation, multi-sensor, and synthetic-data pipelines
• Build evaluation and benchmarking methods that predict real-world transfer, including world-model evaluation and sim-to-real methodology
• Fine-tune and evaluate foundation models on Innodata data through data-quality ablations and scaling studies
• Design adversarial and long-horizon evaluations and convert failure modes into improved data
• Publish benchmarks, methodologies, and papers
• Collaborate with capture, annotation, and synthetic-data teams to operationalize collection and labeling plans

Research Scientist, Robotics – World Models

Principal

• Own the linguistic foundation of Innodata's segmentation and transcription work across languages, domains, and use cases
• Define transcription and segmentation standards, style guides, and annotation conventions
• Establish and run quality frameworks, including rubrics, error taxonomies, adjudication processes, inter-annotator agreement, and human QA at scale
• Own the end-to-end quality lifecycle for transcription and segmentation deliverables
• Design human-in-the-loop workflows for review, correction, and adjudication as ASR quality improves
• Handle difficult cases including accented and dialectal speech, low-resource and multilingual audio, overlapping speech, domain jargon, and noisy acoustic conditions
• Partner with the Speech & Audio Research Scientist to translate model objectives into specifications and assess how transcription methods affect ASR, TTS, and speech/content-understanding models
• Train, calibrate, and mentor expert transcribers and reviewers
• Represent Innodata's transcription and segmentation approach to customers and frontier labs
• Contribute to methodology and best-practice documentation

Principal Speech Data Linguist

Content Marketing Manager

• Review individual social media posts in their entirety, including captions, images, carousels, video, overlaid text, emojis, hashtags, logos, packaging, and spoken content
• Watch/review videos according to the prescribed sampling protocol
• Apply detailed classification taxonomies covering potentially problematic content, including mature themes, body-focused content, suffering and distress, risky behavior, violence, social aggression, and other defined categories
• Identify signals associated with suspected branded content
• Complete reviewability checks and use the appropriate rejection or escalation path when content cannot be accurately assessed
• Apply multi-select labeling rules precisely, including rules governing mutually exclusive or non-combinable labels
• Distinguish between similar concepts based on the written specification rather than personal interpretation or assumptions

Content Moderator – 2 Month Contract

Program Manager

• Own assigned client engagements from kickoff through completion
• Build and manage human data, including data annotation, labeling, and evaluation datasets, for off-the-shelf AI/ML products
• Coordinate data operations, analysts, and subject-matter experts to keep projects on track
• Translate technical concepts for non-technical stakeholders and vice versa
• Balance priorities across multiple clients and projects, proactively identifying and resolving risks
• Use quality metrics, acceptance criteria, and dataset-level analysis to identify gaps and drive improvements
• Contribute to process and delivery improvements beyond day-to-day execution as bandwidth allows

Data Program Manager – AI/ML Delivery

Artificial Intelligence

• Review an assigned scenario and roleplay a natural conversation with two different AI models.
• Conduct comparable conversations with Model A and Model B, keeping the scenario, conversational approach, and overall interaction as consistent as possible.
• Maintain approximately the same number of conversational turns with each model to support a fair comparison.
• Record your voice during each interaction and pay close attention to the quality and behavior of the model's responses.
• Evaluate each model across five defined evaluation dimensions.
• Identify and categorize relevant error clusters that may occur in the audio or conversation.
• Compare the performance of Model A and Model B based on the evaluation criteria and your observations.
• Select the model that demonstrated the stronger overall performance according to the defined evaluation framework.
• Write a detailed rationale explaining your final preference, referencing specific examples and observations from both conversations.
• Apply evaluation guidelines consistently across different scenarios and model interactions