Rockstar

• Own the talent sourcing operation for U.S.-based clients. • Review AI recommendations across inbound and outbound. • Support operations team with new campaign setup. • Shortlist candidates for inbound, InMail outreach, and calibration. • Review candidates against role requirements and prioritize.

• Build and own pipeline through direct selling to tenant and landlord end-users. • Generate client introductions through engagement with brokers, designers, and CRE influencers. • Run daily outbound across multiple channels (phone, email, LinkedIn, direct mail). • Activate a quarterly field strategy, including breakfasts, events, showroom tours, and portfolio walk-throughs. • Operate a disciplined inbound motion, converting high-intent opportunities to closed deals with urgency. • Prospect into high-value landlord portfolios, using existing wins as proof points to expand footprint. • Lead multi-stakeholder sales cycles involving brokers, landlords, tenant leadership, design partners, and internal teams. • Run strong, consultative discovery, shaping demand, not just passively qualifying it. • Deliver clear proposals, quote packages, timelines, and value narratives tailored to different customer personas. • Manage pricing and commercial conversations with confidence and discipline. • Partner closely with Operations to ensure installs, logistics, and on-site execution run smoothly. • Identify delivery or timeline risks early and proactively coordinate internal teams to protect the customer experience.

Role Description Rockstar is hiring a Talent Sourcing Lead to own the talent sourcing operation for our U.S.-based clients. You will work with Rockstar's proprietary recruiting platform to: - Review AI recommendations across inbound and outbound. - Support our operations team with new campaign setup. - Shortlist candidates for inbound, InMail outreach, and calibration. - Review candidates against the role requirements and decide who to prioritize. - Partner closely with the leads for each campaign to make adjustments in priority and strategy when necessary. This is a high-impact role and a cornerstone to Rockstar’s operation. Qualifications - 3–5+ years of experience in talent sourcing or recruitment for U.S.-based companies. - Excited to work in a fast-paced, high-growth, remote startup environment. - Proven success sourcing for technical and non-technical roles. - Strong organizational skills with the ability to manage multiple priorities simultaneously. - Comfortable working U.S. business hours.

Role Description They are seeking a Data Scientist to join the team. Do you love working in machine learning pipelines with big data from day one? Do you have hands-on experience in state-of-the-art NLP (e.g., transformers, BERT, few-shot learning, fine-tuning) and information retrieval (e.g., vector-based semantic search, reranking, hybrid search)? Do you want to continue to work at the frontier of advanced ML and NLP, at a mission-driven startup where they love what they do? If this sounds like you, this might be a dream job! What You’ll Own - Recommender Systems: Ensuring the product makes high-quality recommendations to both job seekers (as a “GPS” for their careers) and employers (as a “talent radar”). - Taxonomies: Using data and AI to model and make sense of the evolving landscape of skills, occupations, and careers in a changing labor market. - Agentic AI Solutions: Prototyping and evaluating new AI-powered functions for job seekers, employers, and case workers. - Data Science Strategy: Serving as an advisor and expert on strategy and roadmap decisions, evaluating major developments in the field, and ensuring the use of state-of-the-art techniques. - Mentorship/Leadership: Acting as a dedicated mentor and tech lead, assisting with the quality, rigor, and acceleration of projects and tasks. Qualifications - 5+ years of combined experience in industry and/or graduate-level research. - Proficiency training and evaluating ML and NLP models using both structured and unstructured data with reproducible results. - Expert-level programming and software engineering skills and a track record of delivering re-usable and well-documented code. - Practical knowledge of using LLMs to accelerate model development, for example through synthesizing training data or automating evaluation. - Proven ability to own a problem space from 0 to 1, translating vague business problems into production models. - Comfortable navigating ambiguity and working with competing priorities. - Strong communication skills to present models and actionable insights to stakeholders with varying levels of technical expertise. Bonus Points - Specific examples of recommendation systems, taxonomies, or AI agents that you have built. - Experience with data engineering, analytics, dashboards, or BI tools. - Experience with A/B testing or causal analysis. - An economics or social sciences background, especially any experience analyzing or modeling labor market data. - Recognized expertise through publications, conference talks, open-source software releases, or other public artifacts related to state-of-the-art tools, models, and techniques. Our Tech Stack - Languages: SQL, Python - Data orchestration: Airflow - Data storage and warehousing: S3, Glue, Redshift, PostgreSQL, MongoDB - Machine learning and experimentation: AWS SageMaker - Visualization and reporting: Looker, QuickSight - Infrastructure: AWS ecosystem Your Education Your alma mater isn’t their focus. Your grit, hunger, and drive are. If you learn continuously, tackle challenges head-on, and know your strengths and gaps intimately—you’re their person. Location United States or Canada (Remote, ET Preferred) Travel Expectations You may be expected to travel up to once per quarter. Compensation The base salary range for this role is $115,000-$140,000. This range reflects the varying levels of expertise and responsibilities that will be determined through the interview process, based on applied experience and other criteria established by the hiring committee. Hiring Journey The hiring process is designed to help you assess whether this role and the culture are the right fit based on your unique skills, mindset, and experiences. They move fast and work with intensity, so they want you to get a real sense of that from the start. - Online Application - Initial Screen with Director of People & Culture - Interview with Hiring Manager - Performance Challenge - Panel Interview - Final Decision Generally, this entire process takes around 4 weeks, although the timing can vary due to specific candidate circumstances. Company Snapshot - Team: 30-50 across US and Canada (hubs in NYC and Toronto) - Customers: Workforce development agencies and intermediaries, government agencies, employers - Industry: SaaS/AI technology - Funding: Bootstrapped 0-1, then raised funding led by JP Morgan - Structure: Growth, Customer Success, Product, Engineering, Data, People & Culture, Finance & Operations Our Core Principles - Be Curious - Drive to Outcomes - Raise the Bar - Speed Matters - Own It - We Over Me Use of AI in Hiring They use artificial intelligence (AI) tools to make the hiring process more efficient, consistent, and equitable—never to replace human judgment. AI is used in the following ways: - Screening support: AI may help compare applications against the skills and experience required for a specific role. These skills are defined by the hiring team for each position. A human reviews each application, with the AI assessment as just one input. - Interview support: In some interviews, they may use an AI notetaker to summarize the discussion so interviewers can focus on being present in the conversation. - Insights, not decisions: AI provides data points to support the team’s evaluation but does not make or recommend final hiring decisions. Every hiring decision is made by people. They will ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, perform essential job functions, and receive other benefits and privileges of employment. Please contact them to request an accommodation. They are proud to be an equal opportunity workplace. They celebrate diversity and are committed to creating an inclusive environment for all employees. They do not discriminate on the basis of race, religion, color, gender identity, sexual orientation, age, disability, veteran status, or other applicable legally protected characteristics. They encourage people of different backgrounds, experiences, abilities, and perspectives to apply.

Role Description This role owns the answer to "why does a [company industry] choose the client over an agency, an in-house hire, ChatGPT, or doing nothing", and turns that into positioning, proof, and a playbook that compounds vertical by vertical. Your mission is to start with an assigned vertical (dentists, med spas, lawyers, etc) and win that space. Document the playbook. Run vertical two against it in half the calendar time, vertical three in half the time again. What This Role is Measured By - An increase in revenue by understanding the customer, aligning the client smartly to their audience, and communicating the benefits of what they do. - A clear, defensible position for the client across done-for-you services & agency partnerships — ICPs named, value props locked, tiers legible to a stranger. - Website conversion lift — measurable improvement in visitor-to-lead metrics. The client wants to scale their organic lead flow. - Supporting sales by translating technology and ICP know-how into effective strategies for the SDRs and BDRs. - Named case studies with quantified outcomes that the sales team uses in every cycle. - Competitive win rate improvement against the top 5 alternatives. - Every release lands with a launch — pipeline attributable to the launch, not just an announcement. - A messaging system that compounds — proof, quotes, and assets organized so Kristy and Nitasha ship faster each quarter. - Faster, better-converting pipeline — messaging tested against live deals, with documented learnings on what works. - Drive the vertical content engine that dogfoods the product. Rank the client's own vertical blogs and terms using their platform. The proof is that they outrank their competitors with their own tool. - Arm sales with the full enablement stack. Pitch deck, one-pagers, ROI calculator, demo script, objection handling, battle cards. Refresh regularly. - Lead pricing and packaging with data. Company Description

• Design, build, and deploy production GenAI systems, including LLM applications, agentic workflows, RAG pipelines, and AI-powered search capabilities. • Architect scalable AI services using modern ML frameworks, model-serving tools, APIs, Docker, Kubernetes, and CI/CD pipelines. • Develop and optimize retrieval systems using embeddings, vector databases, semantic search, reranking, and structured data sources. • Fine-tune, adapt, and evaluate LLMs for domain-specific use cases using prompt engineering, supervised fine-tuning, LoRA / QLoRA, or related methods. • Build automated evaluation frameworks to measure model quality, prompt performance, retrieval accuracy, reasoning reliability, latency, and cost. • Implement observability for AI systems, including tracing, logging, performance monitoring, drift detection, and output-quality review. • Translate prototypes and research concepts into reliable product features that can scale in production. • Partner with product managers, data engineers, backend engineers, analysts, and business stakeholders to define AI capabilities and technical tradeoffs. • Review architecture, provide technical guidance, mentor junior team members, and promote strong engineering practices. • Create clear technical documentation, implementation plans, runbooks, and model lifecycle documentation.

• Assist in developing AI-powered features using Python, LLM tools, ML libraries, APIs, and internal platform services. • Support prompt engineering, prompt testing, model comparison, and evaluation of AI-generated outputs. • Help build and maintain RAG workflows, including document preparation, chunking, metadata tagging, embedding generation, retrieval testing, and result review. • Prepare, clean, format, and validate datasets used for model testing, prompt evaluation, and AI experiments. • Assist with model and workflow evaluation by reviewing outputs, identifying errors, documenting patterns, and comparing performance across approaches. • Write clean, readable Python code for scripts, internal tools, prototypes, experiments, and service components. • Support debugging of AI workflows, data pipelines, API integrations, and model behavior under the guidance of senior engineers. • Participate in code reviews, design discussions, team planning, and documentation efforts. • Learn and apply production engineering practices, including Git workflows, testing, logging, Docker, CI/CD, and deployment basics. • Document experiments, implementation details, findings, and recommendations clearly for technical team members.

Role Description Our client is seeking a Jr. AI Engineer/Jr. Machine Learning Engineer to support the development, testing, and improvement of AI-powered features across their data intelligence platform. This role is designed for an early-career engineer who has strong technical fundamentals, curiosity about GenAI systems, and an interest in learning how production AI products are built and maintained. The Jr. AI Engineer will work closely with senior engineers to assist with: - Prompt experimentation - Data preparation - RAG pipeline support - Model evaluation - Documentation - Debugging - Basic AI service development This role offers hands-on exposure to: - LLMs - Embeddings - Retrieval systems - ML workflows - Production engineering practices Qualifications - 0–2 years of experience in AI engineering, machine learning, software engineering, data science, or a related technical area. - Internship experience, academic work, bootcamp projects, portfolio projects, or open-source contributions are acceptable. - Solid Python programming skills. - Foundational understanding of machine learning, deep learning, NLP, data processing, and model evaluation concepts. - Familiarity with tools or libraries such as PyTorch, TensorFlow, scikit-learn, Hugging Face Transformers, pandas, NumPy, or similar technologies. - Interest in LLMs, GenAI systems, prompt engineering, embeddings, semantic search, RAG, and AI agents. - Ability to work with structured and unstructured data. - Comfort using Git, notebooks, command-line tools, APIs, and collaborative development workflows. - Strong attention to detail, curiosity, problem-solving ability, and willingness to learn from feedback. - Clear written communication skills for documenting technical work and experiment results. Requirements - Portfolio, academic, internship, or project experience involving LLMs, chatbots, semantic search, classification, summarization, automation, or ML workflows. - Exposure to vector databases, embeddings, document processing, information retrieval, or search systems. - Familiarity with Docker, cloud environments, CI/CD concepts, or basic deployment workflows. - Exposure to agent frameworks such as LangGraph, AutoGen, CrewAI, or similar tools. - Coursework or practical experience in machine learning, NLP, statistics, data engineering, computer science, or software engineering. - Interest in security analytics, investigations, data intelligence, fraud detection, or enterprise AI systems. Special Skills or Experience Required - Foundational knowledge of machine learning, deep learning, NLP, LLMs, prompt engineering, and RAG concepts. - Solid Python skills with exposure to ML libraries such as PyTorch, TensorFlow, scikit-learn, Hugging Face Transformers, or similar tools. - Experience through coursework, internships, projects, or portfolio work involving AI, data preparation, model testing, search, or automation. - Ability to document experiments, compare model outputs, support debugging, and learn production ML practices such as Git, APIs, Docker, and CI/CD. Success Measures Success in this role will be measured by: - Consistent contribution to AI experiments - Clean and reliable implementation work - Clear documentation - Improved evaluation support - Effective debugging assistance - Steady growth in production AI engineering skills The role should help increase team capacity while developing strong internal AI engineering talent over time.

Role Description The client is seeking a Sr. AI Engineer/Sr. Machine Learning Engineer to design, build, deploy, and maintain production-grade AI systems across their data intelligence platform. This role will lead the development of LLM-powered applications, agentic workflows, retrieval-augmented generation systems, model evaluation pipelines, and scalable AI services. The ideal candidate combines strong machine learning expertise with practical production engineering experience. This person will own complex technical work from concept through deployment, mentor other engineers, and help define best practices for building reliable, observable, and secure AI systems. Essential Responsibilities - Design, build, and deploy production GenAI systems, including LLM applications, agentic workflows, RAG pipelines, and AI-powered search capabilities. - Architect scalable AI services using modern ML frameworks, model-serving tools, APIs, Docker, Kubernetes, and CI/CD pipelines. - Develop and optimize retrieval systems using embeddings, vector databases, semantic search, reranking, and structured data sources. - Fine-tune, adapt, and evaluate LLMs for domain-specific use cases using prompt engineering, supervised fine-tuning, LoRA / QLoRA, or related methods. - Build automated evaluation frameworks to measure model quality, prompt performance, retrieval accuracy, reasoning reliability, latency, and cost. - Implement observability for AI systems, including tracing, logging, performance monitoring, drift detection, and output-quality review. - Translate prototypes and research concepts into reliable product features that can scale in production. - Partner with product managers, data engineers, backend engineers, analysts, and business stakeholders to define AI capabilities and technical tradeoffs. - Review architecture, provide technical guidance, mentor junior team members, and promote strong engineering practices. - Create clear technical documentation, implementation plans, runbooks, and model lifecycle documentation. Qualifications - 5+ years of experience in machine learning engineering, AI engineering, data science engineering, or a related technical role. - 2+ years of experience building or shipping production GenAI, LLM, or AI-powered systems. - Advanced Python programming skills and experience building maintainable production software. - Hands-on experience with PyTorch, TensorFlow, Hugging Face Transformers, scikit-learn, or similar ML frameworks. - Experience with LLM applications, RAG systems, embeddings, vector databases, prompt engineering, and model evaluation. - Experience deploying AI / ML services using Docker, Kubernetes, CI/CD workflows, APIs, and cloud-native infrastructure. - Strong understanding of classical machine learning, deep learning, NLP, information retrieval, and model validation. - Ability to communicate complex AI concepts clearly to technical and non-technical stakeholders. - Experience mentoring engineers, reviewing technical designs, or leading complex AI engineering initiatives. Preferred Qualifications - Advanced degree in Computer Science, Machine Learning, Artificial Intelligence, Data Science, or a related field. - Experience with agent frameworks such as LangGraph, AutoGen, CrewAI, or similar tools. - Experience with model-serving platforms such as vLLM, BentoML, Triton, Ray Serve, or similar systems. - Familiarity with ML observability, experiment tracking, model monitoring, and prompt/version management tools. - Experience with graph-based retrieval, knowledge graphs, multimodal models, large-scale data processing, or security-focused data products. - Experience with infrastructure-as-code, workflow orchestration, model routing, caching, batching, or quantization. Special Skills or Experience Required - Proven experience building and deploying production GenAI systems, including LLM applications, agentic workflows, and RAG pipelines. - Advanced Python and ML framework experience, including PyTorch, TensorFlow, Hugging Face Transformers, or similar tools. - Experience with LLM fine-tuning, prompt engineering, embeddings, vector databases, semantic search, and model evaluation. - Strong production engineering skills, including Docker, Kubernetes, CI/CD, model serving, observability, latency optimization, and technical leadership. Success Measures Success in this role will be measured by the delivery of reliable AI capabilities, improved model quality, reduced latency and cost, stronger evaluation coverage, improved observability, and the successful mentorship of other engineers. The role should help increase the speed and confidence with which the company can move AI features from prototype to production.

• Own cross-functional analytics projects. Partner with Operations, Sales, Finance, Product, Member Experience, leadership, and other teams to understand business problems, define requirements, analyze data, and deliver durable solutions. • Build trusted data models. Design, build, test, document, and maintain dbt models that transform raw source data into reliable, reusable, analytics-ready datasets. • Work deeply in BigQuery. Use advanced SQL in BigQuery to investigate complex questions, optimize queries, validate data, create reusable logic, and support production-grade reporting. • Support and improve the BI layer. Build and maintain dashboards, reports, semantic definitions, and self-service analytics experiences in Omni and/or Power BI. • Improve metric consistency. Help define business metrics, reconcile conflicting numbers, document assumptions, and ensure stakeholders understand what data means and how to use it. • Monitor and troubleshoot data quality. Work with Fivetran-fed data and downstream dbt/BI assets to identify pipeline issues, data freshness problems, schema changes, schema changes, source system inconsistencies, and reporting defects. • Translate business ambiguity into data solutions. Lead stakeholder discovery, ask sharp questions, clarify tradeoffs, and convert business needs into practical data requirements and deliverables. • Enable better decision-making. Deliver insights and reporting that help teams improve operational performance, sales effectiveness, member experience, financial visibility, and leadership decision-making. • Use AI tools thoughtfully and safely. Use Claude and other AI tools to accelerate analysis, SQL iteration, documentation, test generation, workflow automation, and stakeholder support while validating outputs and protecting data quality. • Raise the bar for the Data team. Contribute to naming conventions, documentation standards, QA practices, metric governance, dbt structure, BI usability, and overall trust in the data ecosystem. • Other duties as assigned.