Location: 439th CES/CER, 250 Patriot Avenue, Westover ARB, MA 01022 (Onsite)

Citizenship: US Citizen (MUST)

Security Clearance: Requires successful completion of NACI/SF85 (Public Trust) background investigation.

Position Overview:

The Support Specialist provides on-site, junior-level technical support to the 439th Civil Engineer Squadron (CES) at Westover ARB to help operate and sustain AI-enabled solutions in a secure DoD environment. Working under guidance from senior engineers and government stakeholders, the role supports day-to-day platform operations, basic data/model updates using established procedures, user support, and documentation for AI/ML-enabled capabilities (e.g., contract analysis, energy management, and snowfall recovery).

This position contributes to mission readiness by helping operationalize and improve critical AI-driven platforms for military engineering, utilities, contracting, and emergency response.

Key Responsibilities:

• AI/ML support: Run established training/evaluation scripts and assist with basic model validation, tuning experiments, and periodic updates under supervision.
• Deployment & integration support: Assist with deployments in secure, on-site/air-gapped environments by following checklists and runbooks; support integration testing and coordinate tickets with DoD IT/cyber teams.
• Documentation: Update and maintain technical notes, user guides, and training materials using provided templates and standards.
• Training support: Assist with recurring user training and help translate technical workflows for non-technical users; capture feedback and improvement requests.
• Security: Follow DoD cybersecurity requirements; protect CUI/FCI; comply with privacy and confidentiality rules.
• Sustainment: Perform basic monitoring and health checks; execute approved patches/updates using runbooks; troubleshoot common issues and escalate complex problems.
• Reporting: Support labor-hour reporting and contract deliverables on Government timelines.
• Coordination: Log, track, and triage support tickets/incidents; communicate status updates; coordinate with the Resources Flight Chief and technical leads to resolve issues.

Required Qualifications:

• Education: Bachelor’s degree in computer science, engineering, data science, or related field.
• Experience: Minimum of 2 years’ direct hands-on experience or commensurate education in artificial intelligence, machine learning, data science, or a related field within secure DoD or comparable environments.
• Technical Skills: Working knowledge of Python; basic Linux command line; Git/version control; ability to run and troubleshoot scripts; familiarity with at least one ML library (e.g., scikit-learn or PyTorch/TensorFlow) and basic data handling (CSV/SQL fundamentals).
• Security & compliance: Ability to follow secure handling procedures for CUI/FCI and comply with DoD/site security requirements; willingness to complete required security and privacy training.
• Communication: Clear verbal and written communication for technical and non-technical audiences; able to draft documentation and provide status updates with guidance.
• Professionalism: Present a professional appearance; display contractor identification badges at all times; adhere to all base regulations and government conduct policies.

Additional Requirements:

• U.S. citizenship; eligible for base access.
• Occasional travel to other AFRC locations, as authorized.
• Compliance with privacy, safety, and physical/environmental security rules.
• Maintain service continuity and follow contract staffing constraints.
• Maintain personally issued computing equipment per contract terms.

Preferred Qualifications:

• Prior support for .mil/.gov AI, data, or software solutions (internships/projects count).
• Familiarity with DoD acquisition/contracting and compliance processes.
• Familiarity with containers (Docker) and/or basic CI/CD concepts; interest in automation/scripting to improve repeatable support tasks.

Toast creates technology to help restaurants and local businesses succeed in a digital world, helping business owners operate, increase sales, engage customers, and keep employees happy.

The Customer Success Data and Analytics (CSDA) team supports Care Operations, Customer Success, Education, and Voice of the Customer. We are building a new generation of data products that extract structured, actionable insight from unstructured customer interactions using NLP, ML, and LLM-based approaches.

As the Principal Data Product Manager, Applied AI, you will own the agenda for how AI-enriched data improves our understanding of the customer experience. You will prototype, validate, and scale a portfolio of AI-powered data products that give our CS organization insight it has never had before. You will also serve as a resource and thought partner for teammates and other teams across Toast who are working with CS data in an AI context, helping make that work faster, more consistent, and more reliable.

This is a hands-on, high-ownership role. You will spend time in Snowflake and Hex building things, and you will spend time with CS leaders explaining what those things mean and why they matter. The right person for this role is equally comfortable doing both.

A Day in the Life (Responsibilities)

Enablement and Shared Practice

• Technical Resource: Serve as the go-to resource on applied AI and NLP with CS data for your CSDA teammates and other teams across Toast.
• Best Practices: Build and maintain a practical library of guidance regarding successful prompts, pitfalls to watch for, and how to structure CS data for AI use cases.
• Market Intelligence: Stay current on how CS data is being used with AI tools across the organization to keep CSDA ahead of the curve.
• Facilitation: Act as a facilitator for AI prototyping work, helping analysts and stakeholders structure experiments so outputs are reliable and usable.

Applied AI and ML Product Development

• Portfolio Ownership: Drive an active portfolio of prototyping work focused on enriching the CSDA data model with insight extracted from unstructured contact data.
• Strategic Focus: Focus on contact classification, intelligent summarization, sentiment analysis, churn signal detection, and product mention extraction.
• End-to-End Design: Manage the pipeline from problem framing and prompt design to embedding generation, classifier training, and final production.
• Stakeholder Validation: Run structured validation processes, including codebook reviews and labeling sessions, to earn trust before products scale.

Scaling and Productionalization

• Readiness Framework: Maintain a clear framework for deciding what is ready to scale based on business value, quality, and maintenance requirements.
• Engineering Partnership: Partner closely with the CSDA data engineering team to scope and hand off production-grade implementations.
• Quality Governance: Own the quality review process for AI products in production, including monitoring for output drift and driving recalibration.
• Product Advocacy: Serve as the face of these products with stakeholders, explaining how they work and being honest about their limitations.

What You’ll Need to Thrive (Requirements)

• Relevant Experience: 6+ years of experience in analytics, data science, ML, or NLP in a data-intensive environment.
• AI Fluency: Practical fluency with LLMs, including prompt engineering, structured output extraction, and model evaluation.
• Product Instincts: You think about what you are building in terms of who uses it and how you will know if it is working.
• Project Leadership: Strong program and stakeholder management skills with the ability to move things forward without formal authority.
• Technical Expertise: Applied experience with text data and NLP/ML techniques such as LLMs, classifiers, or embeddings.
• Core Skills: Expert-level SQL and solid Python proficiency with the ability to build and iterate on ML pipelines independently.
• Communication Mastery: Ability to explain complex classification pipelines to non-technical leaders and push back on requirements when necessary.

What Will Help You Stand Out (Nice to Haves)

• Specific Tooling: Experience with Snowflake Cortex, including COMPLETE and EMBED_TEXT functions.
• Library Familiarity: Familiarity with scikit-learn, spaCy, Hugging Face, or sentence-transformers.
• Domain Expertise: Prior work in a Customer Success, Care, or CX analytics context.
• Workflow Design: Experience building human-in-the-loop validation workflows or labeling interfaces.
• Proven Impact: A track record of prototyping things that made it into production and were successfully used.

AI at Toast

At Toast, one of our company values is that we're hungry to build and learn. We believe learning new AI tools empowers us to build for our customers faster, more independently, and with higher quality. We provide these tools across all disciplines, from Engineering and Product to Sales and Support, and are inspired by how our Toasters are already driving real value with them. The people who thrive here are those who embrace changes that let us build more for our customers; it’s a core part of our culture.

Our Total Rewards Philosophy 
We strive to provide competitive compensation and benefits programs that help to attract, retain, and motivate the best and brightest people in our industry. Our total rewards package goes beyond great earnings potential and provides the means to a healthy lifestyle with the flexibility to meet Toasters’ changing needs. Learn more about our benefits at https://careers.toasttab.com/toast-benefits.

Role overview

The Manager, Data Science will lead an Inventory & Dealer Data Science team focused on developing, deploying, and optimizing machine learning models that power CarGurus’ products and business insights. Sitting within the broader Data Science organization, this team is responsible for modeling automotive marketplace dynamics and dealer behavior. The team owns Machine Learning solutions end-to-end across the ML lifecycle, from R&D to production. This leader is accountable for the team’s strategic direction and impactful delivery, while building a culture of innovation, technical excellence, and collaboration. The Manager partners with Product, Engineering, Analytics, and cross-functional stakeholders to ensure team efforts advance CarGurus’ goals in intelligence-powered products.

What you'll do

• Oversee the development, training, and evaluation of machine learning models covering Inventory and Dealer intelligence. Leverage modeling techniques including recommendations, demand forecasting, churn risk prediction, and valuation algorithms.
• Facilitate experimentation, A/B testing, and measurement of production models, ensuring robust evaluation of business impact
• Build and develop a high-performing team - providing regular coaching, feedback, and performance assessments; ensure equitable access to growth opportunities.
• Drive innovation, continuous improvement, and adoption of best-in-class ML and AI practices. Champion best practices across the organization.
• Participate in roadmap planning, aligning the team’s work with broader business priorities and long-term strategy. Flag resource constraints and escalate tradeoff decisions when needed.
• Act as a visible, accessible leader - communicating objectives, updates, risks, and wins to all stakeholders.
• Partner with peers in the Machine Learning Platform, Product and Data teams, and other stakeholders to facilitate effective cross-team working relationships and represent data science in cross-functional forums.

What you'll bring

• 5+ years of experience in a data science or machine learning engineering role
• 2+ years of people leadership experience
• Deep expertise in machine learning models and their application to classification, regression, ranking, and recommendation problems. Familiarity with standard evaluation metrics and statistical best practices.
• Experience deploying and owning models in production and a working understanding of MLOps practices.
• Strong working knowledge of Python development best practices and the Python ML ecosystem (e.g., scikit-learn, PyTorch, XGBoost, numpy, pandas).
• Experience with cloud platforms, particularly AWS, including deploying, monitoring, and troubleshooting ML models.
• Effective communication and facilitation abilities with both technical and non-technical stakeholders (including data scientists, product management, senior leadership, and engineers).
• Experience contributing to the technical direction for a team and a strategic mindset for prioritizing projects and aligning them with business goals.
• Knowledge of standard software development methodologies and a history of driving continuous improvement.
• Experience in a collaborative, cross-functional environment, with the ability to build effective working relationships and foster an inclusive team culture.