This is Engineering at Lattice

At Lattice, we build software that helps people and organizations thrive. Our engineering team values maintainable systems, strong collaboration, and thoughtful product experiences.

As we expand AI-powered capabilities across our platform, security is a core part of how we design, build, and operate our product. Our Product Security team partners closely with engineering to ensure we ship features that are secure by default and resilient in production.

What You Will Do

You will work closely with product and platform engineering teams to improve the security of Lattice’s applications and services.

Product Security & Engineering Partnership

• Partner with engineers to identify, triage, and remediate security issues in product features and services
• Participate in security reviews and threat modeling for new features and systems
• Perform security-focused code reviews and help identify common vulnerabilities
• Contribute to secure-by-default patterns, libraries, and tooling in our TypeScript-based stack

Security Tooling & Operations

• Help implement and operate security tooling (SAST, DAST, dependency scanning, etc.)
• Support vulnerability management workflows, including internal findings and bug bounty reports
• Assist in investigating security issues and assessing risk and impact

Cloud & Platform Security

• Collaborate with platform and infrastructure teams to improve application and cloud security posture
• Contribute to improving security practices in AWS-based environments

AI/LLM Security

• Assist in identifying and mitigating risks in AI/LLM-powered features, including prompt injection, data leakage, and unsafe output handling
• Apply emerging best practices (OWASP Top 10 for LLM Applications) to real product use cases

Security Enablement

• • Contribute to security guidance, documentation, and training for engineering teams
  • Help improve how security is integrated into the development lifecycle

What You Will Bring to the Table

Experience that is important for you to have at some level:

• 1–3+ years of experience in product security, application security, or software engineering
• Experience writing and maintaining code in JavaScript/TypeScript (or similar languages like Python or Ruby)
• Familiarity with common web and API vulnerabilities (e.g., OWASP Top 10)
• Exposure to security testing tools (SAST, DAST, dependency scanning, etc.)
• Experience working in or with cloud environments (AWS or similar)

Technical Approach

• Ability to identify common security risks and suggest practical mitigations
• Understanding of secure coding practices and basic security controls
• Interest in how security decisions impact real-world product systems

Collaboration & Growth

• Strong communication skills and ability to work closely with engineering teams
• Willingness to ask questions, learn quickly, and take ownership of well-scoped problems
• Ability to contribute to team discussions and share security context effectively

Experience that would be nice to have:

• Experience with modern web architectures (Next.js, NestJS, GraphQL)
• Familiarity with containerization or Kubernetes
• Experience or interest in AI/LLM security, including

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The estimated annual cash salary for this role is $116,500 - $154,000. This position is also eligible for incentive stock options, subject to the terms of Lattice’s applicable plans

Benefits: The Company offers the following benefits for this position, subject to applicable eligibility requirements: Medical insurance; Dental insurance; Vision insurance; Life, AD&D, and Disability Insurance; Emergency Weather Support; Wellness Apps; Paid Parental Leave, Paid Time off inclusive of holidays and sick time; Commuter & Parking Accounts; Lunches in the Office; Internet and Phone Stipend; 401(k) retirement plan; Financial Planning; Learning & Development Budget

*Note on Pay Transparency:

Lattice provides an estimate of the compensation for roles that may be hired as required by state regulations. Compensation may vary based on (a) location, as Lattice factors in specific location when benchmarking compensation for most roles; (b) individual candidate skills and qualifications; and (c) individual candidate experience.

Additionally, Lattice leverages current market data to determine compensation, so posted compensation figures are subject to change as new market data becomes available. The salary, other compensation, and benefits information is accurate as of the date of this posting. Lattice reserves the right to modify this information at any time, subject to applicable law.

#LI-remote

This is Engineering at Lattice

At Lattice, we build software that helps people and organizations thrive. Our engineering team values maintainable systems, strong collaboration, and thoughtful product experiences.

As we expand AI-powered capabilities across our platform, security is a core part of how we design, build, and operate our product. Our Product Security team partners closely with engineering to ensure we ship features that are secure by default and resilient in production.

What You Will Do

You will work closely with product and platform engineering teams to improve the security of Lattice’s applications and services.

Product Security & Engineering Partnership

• Partner with engineers to identify, triage, and remediate security issues in product features and services
• Participate in security reviews and threat modeling for new features and systems
• Perform security-focused code reviews and help identify common vulnerabilities
• Contribute to secure-by-default patterns, libraries, and tooling in our TypeScript-based stack

Security Tooling & Operations

• Help implement and operate security tooling (SAST, DAST, dependency scanning, etc.)
• Support vulnerability management workflows, including internal findings and bug bounty reports
• Assist in investigating security issues and assessing risk and impact

Cloud & Platform Security

• Collaborate with platform and infrastructure teams to improve application and cloud security posture
• Contribute to improving security practices in AWS-based environments

AI/LLM Security

• Assist in identifying and mitigating risks in AI/LLM-powered features, including prompt injection, data leakage, and unsafe output handling
• Apply emerging best practices (OWASP Top 10 for LLM Applications) to real product use cases

Security Enablement

• • Contribute to security guidance, documentation, and training for engineering teams
  • Help improve how security is integrated into the development lifecycle

What You Will Bring to the Table

Experience that is important for you to have at some level:

• 1–3+ years of experience in product security, application security, or software engineering
• Experience writing and maintaining code in JavaScript/TypeScript (or similar languages like Python or Ruby)
• Familiarity with common web and API vulnerabilities (e.g., OWASP Top 10)
• Exposure to security testing tools (SAST, DAST, dependency scanning, etc.)
• Experience working in or with cloud environments (AWS or similar)

Technical Approach

• Ability to identify common security risks and suggest practical mitigations
• Understanding of secure coding practices and basic security controls
• Interest in how security decisions impact real-world product systems

Collaboration & Growth

• Strong communication skills and ability to work closely with engineering teams
• Willingness to ask questions, learn quickly, and take ownership of well-scoped problems
• Ability to contribute to team discussions and share security context effectively

Experience that would be nice to have:

• Experience with modern web architectures (Next.js, NestJS, GraphQL)
• Familiarity with containerization or Kubernetes
• Experience or interest in AI/LLM security, including

Location Requirement:
This role is open to candidates located in British Columbia or Ontario, Canada. At this time, we are only able to hire employees residing in these provinces.

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The estimated annual cash salary for this role is CAD $93,750 - CAD $125,000. This position is also eligible for incentive stock options, subject to the terms of Lattice’s applicable plans

Benefits: The Company offers the following benefits for this position, subject to applicable eligibility requirements: Medical insurance; Dental insurance; Life, AD&D, and Disability Insurance; Natural Disaster Support Program; Wellness Apps; Paid Parental Leave, Paid Time off inclusive of holidays and sick time; Working Remotely Stipend; One time WFH Office Set-Up Stipend; Retirement Plan; Financial Planning; and Learning & Development Budget.

*Note on Pay Transparency:

Lattice provides an estimate of the compensation for roles that may be hired as required by state regulations. Compensation may vary based on (a) location, as Lattice factors in specific location when benchmarking compensation for most roles; (b) individual candidate skills and qualifications; and (c) individual candidate experience.

Additionally, Lattice leverages current market data to determine compensation, so posted compensation figures are subject to change as new market data becomes available. The salary, other compensation, and benefits information is accurate as of the date of this posting. Lattice reserves the right to modify this information at any time, subject to applicable law.

#LI-remote

This is Engineering at Lattice

Lattice’s Engineering team is continuously improving both our product and our craft. We build maintainable, performant systems using modern technologies, and we collaborate closely with product and design to deliver agentic, high quality user experiences.

Our AI Engineering team is building the systems that power how AI works across Lattice. Within the Quality sub-team, we focus on how AI systems are evaluated, measured, and improved over time. You’ll contribute to the infrastructure and tooling that help us understand how our AI performs in production and ensure we’re building reliable, high-quality experiences for our customers.

What You Will Do

AI Evaluation & Quality Systems

• Contribute to AI evaluation pipelines, including offline evals, production tracing, and feedback systems.
• Implement and maintain performance metrics (e.g., response quality, task success, reliability) using established frameworks.
• Help create and maintain evaluation datasets and test cases to identify regressions.
• Analyze results and propose incremental improvements to model and agent quality.

AI Systems & Infrastructure

• Contribute to AI system components such as RAG pipelines, retrieval systems, and multi-step workflows within existing architectures.
• Write clean, maintainable Python code that integrates with LLM providers and internal services.
• Support improvements to system reliability, observability, and performance in production.

Execution & Team Contribution

• Deliver well-scoped projects with guidance from more senior engineers.
• Break down tasks, make steady progress, and be proactive in unblocking yourself by asking for help when needed.
• Contribute to team excellence through code reviews, documentation, and knowledge sharing
• Collaborate with cross-functional partners to ship user-facing features.

What You Will Bring to the Table

Experience

• 2–5 years of professional software engineering experience.
• Experience contributing to production systems as part of a team.
• Exposure to AI/ML systems with a strong interest in LLM-powered products.
• Experience debugging systems, working with data, and iterating on performance.

Technical Skills

• Proficiency in Python or a similar language.
• Strong understanding of LLM concepts (prompting, RAG, evaluation).
• Familiarity with backend systems, APIs, and cloud environments (e.g., AWS, GCP).
• Exposure to logging, monitoring, or debugging tools.
• Interest in learning tools like LangGraph, vector databases, and evaluation platforms.

Ways of Working

• Strong ownership: you reliably deliver high-quality work on well defined tasks, on time and communicate progress clearly.
• Learning mindset: you actively seek feedback and improve quickly. 
• Pragmatic and product-minded: you focus on solving problems effectively and not perfectly.
• Collaborative: you contribute to team discussions and uphold engineering best practices.
• Growth-oriented: you actively seek feedback and expand your skills in AI engineering.

Nice to Have

• Hands-on experience with LLMs, prompt iteration, or MLOps. 
• Familiarity with vector databases or retrieval systems.
• Exposure to experimentation, metrics, or basic statistical analysis.
• Familiarity with TypeScript. Our full-stack engineers use it and cross-pollination is valuable.

Location Requirement:
This role is open to candidates located in British Columbia or Ontario, Canada. At this time, we are only able to hire employees residing in these provinces.

The estimated annual cash salary for this role is CAD $90,000 - CAD $120,000. This position is also eligible for stock options, subject to the terms of Lattice’s applicable plans.

Benefits: The Company offers the following benefits for this position, subject to applicable eligibility requirements: Medical insurance; Dental insurance; Life, AD&D, and Disability Insurance; Natural Disaster Support Program; Wellness Apps; Paid Parental Leave, Paid Time off inclusive of holidays and sick time; Working Remotely Stipend; One time WFH Office Set-Up Stipend; Retirement Plan; Financial Planning; and Learning & Development Budget.

Note on Pay Transparency:
Lattice provides an estimate of the compensation for roles that may be hired as required by regulations. Compensation may vary based on (a) location, as Lattice factors in specific location when benchmarking compensation for most roles; (b) individual candidate skills and qualifications; and (c) individual candidate experience. Additionally, Lattice leverages current market data to determine compensation, so posted compensation figures are subject to change as new market data becomes available. The salary, other compensation, and benefits information is accurate as of the date of this posting. Lattice reserves the right to modify this information at any time, subject to applicable law.

Lattice welcomes and encourages applications from people with disabilities. Accommodations are available on request for candidates taking part in all aspects of the recruitment and selection process.

#LI-Remote

This is Engineering at Lattice

Lattice’s Engineering team is continuously improving both our product and our craft. We build maintainable, performant systems using modern technologies, and we collaborate closely with product and design to deliver exceptional user experiences.

Our AI Engineering team is building the systems that power how AI works across Lattice. We’ve laid the foundations: traces are flowing and evals are running - and we’re now focused on defining how our AI products are measured, improved, and trusted at scale. This is a high-ownership role where you’ll help shape evaluation methodology, agent architecture, and the core systems that determine how AI performs in production.

What You Will Do

Evaluation Infrastructure

• Design and ship a robust, end-to-end AI evaluation framework, covering offline evals, production tracing, and human-in-the-loop feedback loops, connected across all of Lattice’s AI use cases.
• Define and instrument the metrics that actually matter: agent task completion, hallucination rates, response quality, user engagement, and downstream business outcomes.
• Build and maintain evaluation datasets, test harnesses, and automated scoring pipelines to catch regressions before they ship.
• Identify and surface the drivers of agent quality improvement, giving the team clear signals on where to invest.

Agent Architecture & Infrastructure

• Architect and implement reusable agent infrastructure: multi-turn conversation workflows, recommendation services, LLM DAGs, and standardized agent topology patterns using LangGraph.
• Build and scale RAG pipelines and retrieval infrastructure, including vector store management and retrieval quality optimization.
• Make principled build vs. buy decisions across LLM providers, agent frameworks, and evaluation tooling, balancing capability, cost, latency, and vendor risk.
• Contribute to production AI systems with a strong focus on reliability, observability, and performance, not just prototypes.

Technical Leadership & Collaboration

• Own projects end-to-end: scope them, drive them to completion, and bring in the right people at the right time.
• Partner with engineering leads and managers to inform technical direction on agent quality and evaluation strategy you’ll be expected to hold intelligent, substantive conversations about methodology, not just implementation.
• Raise the AI engineering bar across the broader team through code review, documentation, and thoughtful technical debate.

What You Will Bring to the Table

Experience

• 5+ years of professional software engineering experience with significant time spent on production AI/ML systems.
• Deep hands-on experience with LLM-based systems: prompt engineering, RAG pipelines, agent orchestration, evaluation metrics, and model fine-tuning.
• Proven ability to work with data and understand statistics, especially in experiments.
• Proven ability to build and operate agentic AI systems in production: multi-step workflows, multi-agent topologies, and the failure modes that come with them.
• Strong command of AI evaluation: you’ve built eval frameworks before, you know the difference between a good eval and a vanity metric, and you have opinions about it.
• Production-grade Python engineering: clean, maintainable, testable code.

Technical Skills

• LangGraph or comparable agent orchestration frameworks. You’ve built real agent workflows with it, not just tutorials.
• LangSmith or comparable LLM observability tooling for tracing, evaluation, and debugging.
• Reads AI papers & blogs regularly and is a trusted source of AI trends.
• Vector databases (Pinecone or similar) and retrieval system design.
• AWS ecosystem or other cloud infrastructure (ex GCP). Comfortable with lambdas, queues, and cloud-native architecture.
• Familiarity with TypeScript is a plus. Our full-stack engineers use it and cross-pollination is valuable.

Ways of Working

• Clear eyes: you see problems as they are, not as you’d like them to be. You surface hard truths early and address them directly.
• Ship, shipmate, self: you prioritize the product and your teammates. Low ego, high ownership.
• You’re as comfortable in ambiguity as you are in well-defined problems: early foundations mean you’ll encounter both.
• Strong technical communication: you can debate evaluation methodology with an AI lead and explain it clearly to an EM in the same afternoon.

Nice to Have

• Experience with RLHF, LoRA, or other model adaptation techniques.
• Background in traditional ML (supervised/unsupervised, neural networks) and knowing when an LLM is overkill.
• Experience with MLOps tooling: MLflow, DataDog, CI/CD pipelines for model deployment.
• Published work, conference talks, or open-source contributions in AI/ML.
• Experience in HR tech, people analytics, or other domains where data quality and trust are critical.

Location Requirement:
We are not able to hire candidates based in the San Francisco Bay Area or the New York Metro Area for this role. 

The estimated annual cash salary for this role is USD $135,500 - $169,500. This position is also eligible for incentive stock options, subject to the terms of Lattice’s applicable plans.

Benefits: The Company offers the following benefits for this position, subject to applicable eligibility requirements: Medical insurance; Dental insurance; Vision insurance; Life, AD&D, and Disability Insurance; Emergency Weather Support; Wellness Apps; Paid Parental Leave, Paid Time off inclusive of holidays and sick time; Commuter & Parking Accounts; Lunches in the Office; Internet and Phone Stipend; 401(k) retirement plan; Financial Planning; Learning & Development Budget

Note on Pay Transparency:
Lattice provides an estimate of the compensation for roles that may be hired as required by regulations. Compensation may vary based on (a) location, as Lattice factors in specific location when benchmarking compensation for most roles; (b) individual candidate skills and qualifications; and (c) individual candidate experience. Additionally, Lattice leverages current market data to determine compensation, so posted compensation figures are subject to change as new market data becomes available. The salary, other compensation, and benefits information is accurate as of the date of this posting. Lattice reserves the right to modify this information at any time, subject to applicable law.

Lattice welcomes and encourages applications from people with disabilities. Accommodations are available on request for candidates taking part in all aspects of the recruitment and selection process.

#LI-Remote

This is Engineering at Lattice

Lattice’s Engineering team is continuously improving both our product and our craft. We build maintainable, performant systems using modern technologies, and we collaborate closely with product and design to deliver exceptional user experiences.

Our AI Engineering team is building the systems that power how AI works across Lattice. We’ve laid the foundations: traces are flowing and evals are running - and we’re now focused on defining how our AI products are measured, improved, and trusted at scale. This is a high-ownership role where you’ll help shape evaluation methodology, agent architecture, and the core systems that determine how AI performs in production.

What You Will Do

Evaluation Infrastructure

• Design and ship a robust, end-to-end AI evaluation framework, covering offline evals, production tracing, and human-in-the-loop feedback loops, connected across all of Lattice’s AI use cases.
• Define and instrument the metrics that actually matter: agent task completion, hallucination rates, response quality, user engagement, and downstream business outcomes.
• Build and maintain evaluation datasets, test harnesses, and automated scoring pipelines to catch regressions before they ship.
• Identify and surface the drivers of agent quality improvement, giving the team clear signals on where to invest.

Agent Architecture & Infrastructure

• Architect and implement reusable agent infrastructure: multi-turn conversation workflows, recommendation services, LLM DAGs, and standardized agent topology patterns using LangGraph.
• Build and scale RAG pipelines and retrieval infrastructure, including vector store management and retrieval quality optimization.
• Make principled build vs. buy decisions across LLM providers, agent frameworks, and evaluation tooling, balancing capability, cost, latency, and vendor risk.
• Contribute to production AI systems with a strong focus on reliability, observability, and performance, not just prototypes.

Technical Leadership & Collaboration

• Own projects end-to-end: scope them, drive them to completion, and bring in the right people at the right time.
• Partner with engineering leads and managers to inform technical direction on agent quality and evaluation strategy you’ll be expected to hold intelligent, substantive conversations about methodology, not just implementation.
• Raise the AI engineering bar across the broader team through code review, documentation, and thoughtful technical debate.

What You Will Bring to the Table

Experience

• 5+ years of professional software engineering experience with significant time spent on production AI/ML systems.
• Deep hands-on experience with LLM-based systems: prompt engineering, RAG pipelines, agent orchestration, evaluation metrics, and model fine-tuning.
• Proven ability to work with data and understand statistics, especially in experiments.
• Proven ability to build and operate agentic AI systems in production: multi-step workflows, multi-agent topologies, and the failure modes that come with them.
• Strong command of AI evaluation: you’ve built eval frameworks before, you know the difference between a good eval and a vanity metric, and you have opinions about it.
• Production-grade Python engineering: clean, maintainable, testable code.

Technical Skills

• LangGraph or comparable agent orchestration frameworks. You’ve built real agent workflows with it, not just tutorials.
• LangSmith or comparable LLM observability tooling for tracing, evaluation, and debugging.
• Reads AI papers & blogs regularly and is a trusted source of AI trends.
• Vector databases (Pinecone or similar) and retrieval system design.
• AWS ecosystem or other cloud infrastructure (ex GCP). Comfortable with lambdas, queues, and cloud-native architecture.
• Familiarity with TypeScript is a plus. Our full-stack engineers use it and cross-pollination is valuable.

Ways of Working

• Clear eyes: you see problems as they are, not as you’d like them to be. You surface hard truths early and address them directly.
• Ship, shipmate, self: you prioritize the product and your teammates. Low ego, high ownership.
• You’re as comfortable in ambiguity as you are in well-defined problems: early foundations mean you’ll encounter both.
• Strong technical communication: you can debate evaluation methodology with an AI lead and explain it clearly to an EM in the same afternoon.

Nice to Have

• Experience with RLHF, LoRA, or other model adaptation techniques.
• Background in traditional ML (supervised/unsupervised, neural networks) and knowing when an LLM is overkill.
• Experience with MLOps tooling: MLflow, DataDog, CI/CD pipelines for model deployment.
• Published work, conference talks, or open-source contributions in AI/ML.
• Experience in HR tech, people analytics, or other domains where data quality and trust are critical.

Location Requirement:
This role is open to candidates located in British Columbia or Ontario, Canada. At this time, we are only able to hire employees residing in these provinces.

The estimated annual cash salary for this role is CAD $123,750 - CAD $165,000. This position is also eligible for stock options, subject to the terms of Lattice’s applicable plans.

Benefits: The Company offers the following benefits for this position, subject to applicable eligibility requirements: Medical insurance; Dental insurance; Life, AD&D, and Disability Insurance; Natural Disaster Support Program; Wellness Apps; Paid Parental Leave, Paid Time off inclusive of holidays and sick time; Working Remotely Stipend; One time WFH Office Set-Up Stipend; Retirement Plan; Financial Planning; and Learning & Development Budget.

Note on Pay Transparency:
Lattice provides an estimate of the compensation for roles that may be hired as required by regulations. Compensation may vary based on (a) location, as Lattice factors in specific location when benchmarking compensation for most roles; (b) individual candidate skills and qualifications; and (c) individual candidate experience. Additionally, Lattice leverages current market data to determine compensation, so posted compensation figures are subject to change as new market data becomes available. The salary, other compensation, and benefits information is accurate as of the date of this posting. Lattice reserves the right to modify this information at any time, subject to applicable law.

Lattice welcomes and encourages applications from people with disabilities. Accommodations are available on request for candidates taking part in all aspects of the recruitment and selection process.

#LI-Remote