The Engineering Challenge
You will be responsible for architecting and building the engine from the ground up. You must solve three specific constraints:

1. The Data Reality: You will not have clean studio data. You must build a pipeline that can ingest "noisy" real-world audio (radio archives, podcasts, street interviews) and autonomously clean, align, and diarize it to create a high-fidelity training set.
2. The Linguistic Complexity: The model must handle Code-Switching (fluidly mixing two languages in one sentence) and Tonal markers without breaking prosody. You must understand how to modify tokenizers to respect these nuances.
3. The Inference Economics: We are not burning venture capital on infinite compute. You must quantize and optimize the model to run on consumer-grade GPUs with low latency. Efficiency is a constraint, not a nice-to-have.

What You Will Own

• End-to-End Pipeline: From raw audio ingestion to served API response.
• Model Fine-Tuning: Adapting foundation models to highly specific, low-resource dialects.
• Inference Architecture: Building a stateless, containerized inference server that handles concurrent requests with sub-200ms latency.

The DNA We Need

• Systems Thinker: You don't just train models; you build products. You understand how the model sits inside a container, how the API handles backpressure, and how the tokenizer affects the runtime.
• Data Realist: You know that 80% of the work is in the dataset. You are comfortable writing custom scripts to slice, denoise, and filter terabytes of audio.
• First-Principles Optimiser: You understand why a model is slow. You are comfortable with quantization, distillation, and kernel-level optimizations to squeeze performance out of limited hardware.

Who You Are (Must-Haves)

• 5 plus years building ML or applied AI systems in production
• Startup experience working on a core product, not a side project
• Strong Python skills and experience integrating with backend systems
• Hands-on experience with AI-assisted coding tools like Cursor or Claude Code
• Fintech experience or strong personal investment background
• Comfortable owning outcomes, not just tasks

Nice-to-Haves (Not Deal Breakers)

• Experience with LLM reasoning systems or knowledge graphs
• Exposure to Golang-based ML integrations
• Prior experience supporting investor-facing products

Qualifications

• 5+ years of experience as a senior engineer in production systems.
• Strong backend and systems design skills.
• Hands-on experience with LLM APIs, agent frameworks, or RAG pipelines.
• Experience building real-time systems (WebSockets, pub/sub, queues).
• Strong knowledge of TypeScript, Python, Go, or similar backend languages.
• Experience with cloud-native architectures and event-driven systems.
• You care deeply about latency, reliability, and correctness.
• Product-minded: you think in workflows, not demos.
• Comfortable working in fast-moving, high-ownership environments.
• Excellent communication and collaboration skills.

• You have 5+ years of experience in a highly quantitative, hands-on role (Actuarial Science, Healthcare Data Science, or ML Engineering) with a track record of shipping production-grade analytical or AI solutions.
• You possess Government Programs Exposure: Direct experience or exposure to health insurance economics, claims, and risk adjustment within Medicare Advantage, Medicaid, or ACA/Marketplace environments.
• You have Modern AI Experience: Experience with LLMs or Generative AI in healthcare or insurance workflows, particularly where explainability, auditability, and operational adoption are critical.
• You are Technically Fluent: High proficiency in Python and SQL, with practical experience developing, deploying, and maintaining AI models in an operational environment.
• You possess Business Intuition: You understand the "why" behind the numbers—you can connect a data anomaly to its underlying financial or actuarial impact without needing a translator.

Skills and Experience

• 3 or more years of full-time experience working in a structured product development environment on a product that had frequent releases
• Hands-on experience with LLM APIs (e.g., OpenAI, Anthropic, Google) and/or AI agent frameworks — including prompt engineering, context management, and evaluation
• Experience with modern programming languages (in this role, you’ll use both Python and TypeScript), as well as experience with AI-enabled coding
• A strong understanding of both the scientific method and software engineering principles, with a proven ability to balance experimental rigor and rapid production deployment
• Experience collaborating with partners from different job functions, and the ability to translate business requirements into technical builds
• Ability to thrive in an entrepreneurial environment, showing a tendency toward speed and action while achieving clarity from ambiguity

Nice to Have:

• Experience with conversational AI voice agents
• A track record of transforming raw, unstructured data sources into actionable intelligence 
• Experience in healthcare, healthtech, or other regulated domains

What We’re Looking For

Required Mindset

• Curious about how AI systems “think” and retrieve information
• Comfortable experimenting and iterating
• Able to explain complex systems simply

Skills & Experience

• Experience with:
  • SEO, content strategy, or technical SEO
  • OR AI/LLMs, prompt engineering, or knowledge graphs
• Strong understanding of:
  • Semantic search
  • Entity-based SEO
  • Content structuring
• Familiarity with tools like:
  • Google Search Console
  • Ahrefs / SEMrush / Similar
  • AI chat tools (ChatGPT, Perplexity, Gemini, Claude)

What We’re Looking For

• 5+ years of experience in AI research, with a strong background in LLM research
• Advanced proficiency in Python and hands-on experience with leading frameworks and libraries, such as PyTorch, Hugging Face, torchtune, and vLLM
• M.Sc. or Ph.D. in Computer Science, Data Science, Statistics, Engineering, Mathematics, Physics, or a related field. Preferably with applied focus on machine learning, computer vision, NLP, or deep learning
• Fast learner with excellent problem-solving skills
• Positive attitude, intellectual curiosity, and eagerness to learn and share knowledge
• Passion for medicine, health, and wellbeing, with a drive to make a meaningful impact
• Proven experience translating deep learning research into reliable, high-impact production systems
• Collaborative team member who contributes to shared technical decisions and knowledge sharing
• (Preferred) Experience with medical data or medical AI

We’d Love for You to Have:

• Eight plus years experience with AI/ML: Deep understanding of machine learning techniques, especially those applicable to healthcare
• Healthcare Expertise: Substantial experience in healthcare technology, clinical workflows, or related fields
• Production AI Experience: Proven track record of taking AI models / products from concept to production in complex environments
• Technical Leadership: Experience leading data science and engineering teams
• Business Acumen: Ability to translate technical capabilities into tangible business value
• Collaborative Mindset: Ability to work effectively with diverse teams, holding strong opinions loosely and valuing input from others
• Communication Skills: Exceptional ability to communicate complex AI concepts to diverse audiences
• Regulatory Knowledge: Familiarity with healthcare regulations affecting AI deployment (HIPAA, FDA, etc.)
• Ethics & Governance: Strong understanding of ethical AI principles and governance frameworks

What You’ll Do:

• Own multiple projects from end to end: brainstorming product concepts, generating POCs to be evaluated and owning builds to reach a minimum viable product.
• Build full-stack applications (backend, frontend, data layer, infra, deployment, and monitoring).
• Experiment with AI functionality on each case: classical ML when needed (forecasting, scoring, classification), plus LLM/agent workflows and evaluation loops
• Establish engineering standards: code quality, CI/CD hygiene, logging/analytics, basic security
• Work directly with design partners and ship weekly updates

Requirements

• You should have:

  • 3+ years shipping full-stack products end-to-end
  • Strong Python and modern web stack (React/Next.js + APIs + DB + deployments)
  • Data processing and Machine learning fundamentals (pandas, Sklearn, pytorch/tensorflow, model evaluation)
  • Experience experimenting with LLMs and Agent frameworks (Open AI SDK, Google ADK, Langchain/Langraph, MCP)
  • Comfort with ambiguity, fast cycles, and customer-facing iteration

• What Success Looks Like:

  • Ship multiple POCs that clearly validate feasibility and ROI
  • Bring at least one concept to a usable MVP with design-partner feedback loops

What You'll Do 

• Design, architect, and deploy AI-powered systems that directly impact member experience and operational efficiency
• Build and optimize LLM-driven applications using advanced prompt engineering, RAG architectures, and agentic workflows
• Design scalable AI pipelines using modern orchestration frameworks (LangChain, LangSmith, LlamaIndex, etc.)
• Architect multi-agent systems capable of reasoning, planning, and executing complex multi-step tasks
• Implement evaluation and monitoring frameworks to measure AI performance, detect hallucinations, and ensure reliability at scale
• Own cloud-native AI infrastructure for performance, scalability, cost efficiency, and security
• Partner with Product and Engineering leadership to identify and execute high-impact AI initiatives
• Stay at the frontier of the AI ecosystem and translate emerging capabilities into production-ready systems
• Establish internal standards, tooling, and best practices for AI development across the organization

What You'll Bring 

• Bachelor’s or Master’s degree in Computer Science, Information Systems, Business Administration, or a related field
• 5+ years of professional software engineering experience, including meaningful ownership of production systems
• Significant hands-on experience building and deploying AI/ML systems in production
• Strong proficiency in Python and TypeScript (Next.js a plus)
• Deep experience with LLM APIs (OpenAI, Anthropic, AWS Bedrock) and orchestration frameworks (LangChain, LangSmith, LlamaIndex)
• Strong understanding of advanced prompt engineering techniques and LLM optimization strategies
• Experience designing and deploying RAG systems, vector databases, and semantic search applications
• Cloud infrastructure expertise (AWS preferred): Lambda, Step Functions, API Gateway, DynamoDB, S3
• Strong understanding of LLM behavior: tokenization, embeddings, context limits, evaluation, and hallucination mitigation
• Demonstrated track record of shipping scalable, production-grade AI systems — not prototypes or demos

Nice to Have

• Experience with infrastructure as code (AWS CDK, SAM, Terraform)
• Experience in healthcare, insurance, or regulated environments
• Experience fine-tuning models or building custom model pipelines
• Familiarity with observability tools (DataDog, CloudWatch, LangSmith)
• Contributions to open-source AI projects or published technical work

• Design and execute the enterprise AI enablement strategy, ensuring employees have the skills and confidence to leverage AI tools for productivity, efficiency, and innovation
• Lead the rollout and adoption of enterprise AI tools, partnering with IT, Communications, and business leaders to drive engagement and measurable impact
• Create learning pathways, resources, and just-in-time training that meet employees where they are—from AI-curious to power users
• Develop AI fluency programs that help employees understand when and how to use AI effectively, including prompt engineering, workflow integration, and critical evaluation of AI outputs
• Establish metrics and feedback loops to measure AI adoption, productivity gains, and employee confidence

• Design, build, and facilitate leadership development programs that meet leaders where they are in their leadership journey—from first-time managers stepping into leadership for the first time to senior leaders driving enterprise strategy
• Upskill leaders on the high-value human work of leadership: building trust, developing talent, having courageous conversations, coaching for performance, and creating environments where people do their best work
• Enable leaders to leverage AI tools effectively so they can spend less time on administrative tasks and more time on the work that matters—leading, developing, and connecting with their teams
• Partner with People Business Partners to identify leadership capability gaps and design targeted interventions
• Facilitate workshops, cohort programs, and experiential learning, and build scalable resources—toolkits, guides, self-directed learning—that support leaders in the moments that matter

• Rethink and transform the Learning & Development function through an AI-first lens
• Leverage AI to create more personalized, efficient, and impactful learning experiences
• Build systems that enable continuous learning, just-in-time support, and knowledge sharing across the organization
• Partner cross-functionally to embed learning and development into the employee journey

• 7+ years of experience in Learning & Development, Organizational Development, or a related field, with progressive responsibility
• 3+ years of experience designing and facilitating leadership development programs
• Demonstrated experience with AI enablement, digital transformation, or technology adoption initiatives
• Strong AI fluency—not just understanding what AI can do, but hands-on experience using AI tools to enhance your own work and helping others do the same
• Track record of designing and delivering training programs that drive measurable behavior change
• Experience working in fast-paced, high-growth, or transformation environments

• Strategic thinking: Ability to connect workforce development initiatives to business outcomes
• Facilitation excellence: Skilled at designing and leading engaging workshops, training sessions, and cohort experiences for diverse audiences, including senior leaders
• AI-native mindset: Comfortable experimenting with AI tools, staying current on capabilities, and translating technology into practical applications for non-technical audiences
• Human-centered approach: Deep belief that technology should amplify human potential, not replace human connection
• Strong communication: Ability to make complex topics accessible and inspire action across all levels of the organization
• Data-driven: Comfort with measuring impact and using data to iterate on programs
• Collaboration: Proven ability to partner across functions (IT, Communications, HR, business leaders) to drive enterprise initiatives

• Experience in healthcare, technology, or high-growth startup environments
• Familiarity with enterprise AI tools (e.g., Microsoft Copilot or similar)
• Coaching certification or executive coaching experience
• Experience with instructional design and adult learning principles
• Background in change management methodologies

You Will: Key Responsibilities & Impact

• Architect and build next-generation Conversational AI products that push the boundaries of what's possible
• Lead and mentor engineering teams, setting technical direction and best practices
• Partner with Product to shape requirements and translate vision into scalable solutions
• Design cloud-native architectures that are resilient, scalable, and built for the future
• Integrate cutting-edge AI tools and services, championing their adoption across the team
• Drive engineering excellence through comprehensive test automation and quality-first delivery

You Have: Required Skills & Qualifications

• 10+ years building full-stack applications with Java and Node.js (Python and Vue.js are a plus)
• Bachelor's or Master's degree in Computer Science, Communications, or related field
• 5+ years designing and architecting enterprise-grade SaaS Products
• 5+ years shipping AI products to production
• Deep experience with cloud platforms (GCP experience is a plus)
• Background in data analytics and reporting tools, especially for contact centers and conversational analytics (nice to have)
• Proven track record leading engineering team as technical expert

Skills and Experience

• 3 or more years of full-time experience working in a structured product development environment on a product that had frequent releases
• Hands-on experience with LLM APIs (e.g., OpenAI, Anthropic, Google) and/or AI agent frameworks — including prompt engineering, context management, and evaluation
• Experience with modern programming languages (in this role, you’ll use both Python and TypeScript), as well as experience with AI-enabled coding
• A strong understanding of both the scientific method and software engineering principles, with a proven ability to balance experimental rigor and rapid production deployment
• Experience collaborating with partners from different job functions, and the ability to translate business requirements into technical builds
• Ability to thrive in an entrepreneurial environment, showing a tendency toward speed and action while achieving clarity from ambiguity

Nice to Have:

• Experience with conversational AI voice agents
• A track record of transforming raw, unstructured data sources into actionable intelligence 
• Experience in healthcare, healthtech, or other regulated domains

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What You Bring

• Proven Track Record in Large Deals: 7+ years managing enterprise sales cycles with experience closing $5M–$20M+ annual agreements spanning multiple concurrent projects.
• Expert in Multi-Threaded Sales: Demonstrated success engaging across research leaders, engineering stakeholders, procurement teams, and C-suite executives.
• Consultative & Strategic Approach: Adept at translating technical needs (AI safety, evals, data quality, benchmarks) into commercial frameworks that resonate with business and technical leaders alike.
• Operational Rigor: Ability to manage complex deal structures, ensure visibility across 20+ active workstreams, and coordinate with internal delivery and product teams to ensure flawless execution.
• Domain Expertise: Experience selling AI, ML, or human-data services to advanced research orgs and technical decision-makers.
• Full-Cycle Deal Leadership: Skilled in qualification, solution design, proposal building, negotiation, procurement navigation, and customer success alignment.
• Executive Communication: Excellent written and verbal communicator with the ability to craft compelling narratives for both technical and business stakeholders.

What You'll Bring

• 8+ years of experience in data science, analytics, or a related AI field
• Strong SQL and Python skills; experience with data visualization tools (e.g., Looker, Tableau, Metabase) for data storytelling
• Experience designing and running A/B tests or correlational analyses
• Familiarity with LLM evaluation methodologies or AI quality assurance
• Experience working with research teams or supporting program evaluation
• Clear communication skills for translating data into insights for non-technical stakeholders

Nice to Have

• Experience in education, workforce development, or public sector contexts
• Familiarity with FERPA, COPPA, or student data privacy requirements
• Experience with knowledge graphs or RAG systems
• Background in qualitative research support or mixed-methods evaluation

ABOUT YOU:

• 6–15 years of experience in technical, customer-facing, or product leadership roles, with a proven track record of driving projects from 0 to 1 and delivering measurable business impact
• Experienced in managing enterprise customer deployments, integrations, and cross-functional initiatives, balancing customer urgency with long-term scalability
• Commercially minded, you understand how successful deployments unlock renewals, expansions, and customer advocacy
• Strong analytical skills who defers to using data to inform decisions, define success metrics, and drive continuous improvement
• Exceptional project management and organizational skills — able to simplify complexity, prioritize competing initiatives, and hold teams accountable while setting clear strategic direction
• Relentless ownership mindset: no task is too small, hands-on when needed, and equally skilled at leading teams, coaching talent, and partnering with executives
• Working knowledge of APIs, data flows, and light-coding experience (JavaScript, Python, or similar)

Tech Stack:

• Infrastructure/Systems: AWS (ECS, Bedrock, Cognito, etc.), Docker, Github Actions
• Languages/Frameworks: Python, Django, Celery, django-ninja, django-tenants
• Database/Storage: PostgreSQL (AWS RDS), S3
• Development Tools: Github, Jira, CoderabbitAI, Tusk, Claude

What we're looking for:

• 5+ years experience building and deploying applications in production in a backend engineering / data engineering capacity
• Relevant experience with developing LLM-based systems for ingesting and evaluating unstructured records for industry-specific use cases and integrating them with user-facing features
• Deep understanding of the limitations of using LLMs and the best practices for using them for reliable, consistent, and accurate outputs
• Customer obsessed and motivated to build a best-in-class model for behavioral health clinical assessments in the healthcare space
• A collaborative team player with a focus on delivering measurable results

Requirements

• You have designed and built scalable, general-purpose search or retrieval systems end-to-end.
• You bring deep expertise in AI-powered search methodologies, including LLMs, embeddings, vector databases, and retrieval-augmented generation.
• You have a strong understanding of indexing, query processing, and ranking architectures.
• You’re a hands-on engineer with experience leading or scaling systems and teams in fast-moving environments.
• You’ve worked within or closely alongside world-class AI/search organizations (e.g., OpenAI, Anthropic, Perplexity, xAI, Google DeepMind, etc.).
• You’re proficient in Python, SQL, Docker, and microservices, with familiarity in function/tool calling from an LLM.
• You communicate with clarity and thrive in a highly collaborative, cross-functional setting.

Nice to Haves

• Experience building retrieval or ranking systems specifically for publisher or adtech ecosystems.
• Familiarity with distributed vector search systems or hybrid retrieval frameworks.
• Prior startup or early-stage product experience—comfortable building with ambiguity and speed.
• Background in reinforcement learning, personalization, or recommendation systems.