[All remote jobs](https://jobicy.com/jobs.md)[![Salesloft logo](https://jobicy.com/data/server-nyc0409/galaxy/mercury/2022/03/8f0ff4d3ba38b945acfa9bbe11e39be1.jpeg)](https://jobicy.com/company/salesloft.md)[Salesloft](https://jobicy.com/company/salesloft.md) # Principal Software Engineer, AI Review the role, location requirements, compensation details, and application process before deciding whether this opportunity fits your next career move. [Apply for this job](#job-application)[View company](https://jobicy.com/company/salesloft.md)ShareRemote from[USA](https://jobicy.com/job-region/usa.md)SalaryUSD 165k–185k / yrDepartment[Software Engineering](https://jobicy.com/categories/engineering.md)EmploymentFull TimeExperienceSeniorPublished26 Oct 2026Apply before25 Nov 2026Listing views63Application actions1Application toolkit ## Make your next move. Prepare your resume, explore your fit, and draft a cover letter for this opportunity. AI Summary ## The role, at a glance. Salesloft is seeking a Principal Software Engineer to define the technical architecture for production AI capabilities in its Predictive Revenue System. The role leads design of scalable model-serving platforms, LLM-powered features, agentic workflows, evaluation systems, and AI guardrails. This senior individual contributor will solve ambiguous, research-adjacent engineering problems while establishing standards and mentoring senior engineers. Success requires deep expertise in distributed systems, production ML/LLM infrastructure, cloud platforms, and reliable operation of probabilistic systems. ## Role DNA A quick view of the complexity, pace, ownership and collaboration implied by the job description. ### Job Complexity 5/5EasyHard ### Pace & Pressure 5/5RelaxedFast-paced ### Autonomy Level 5/5GuidedFull ownership ### Communication Load 5/5IndependentCollaborative AI insightThis is a principal-level, frontier AI engineering role with responsibility for foundational architectural decisions across an enterprise platform. It requires balancing rapid experimentation with production reliability, safety, scalability, and cross-functional technical leadership. ## Salary analysis Estimated compensation compared with the broader US market for similar roles. Estimated job median (per year)Below market$175,000US market range (per year)$190k–$260k0$286k AI insightThe yearly pay period is inferred from the job context; it was not explicitly stated by the employer. The employer discloses a base-pay range of $165,000–$185,000 USD. The posting does not state a cadence, so yearly pay is inferred from the US-based full-time principal engineering context and the magnitude of the range. Estimated US market compensation for comparable principal AI software engineering base-pay roles is approximately $190,000–$260,000 per year; bonus and equity may be additional. ## Core skills Skills and capabilities most closely associated with this opportunity. [AI architecture](https://jobicy.com/jobs?search_keywords=AI%20architecture.md)[Large language models](https://jobicy.com/jobs?search_keywords=Large%20language%20models.md)[Machine learning systems](https://jobicy.com/jobs?search_keywords=Machine%20learning%20systems.md)[Distributed systems](https://jobicy.com/jobs?search_keywords=Distributed%20systems.md)[Model serving](https://jobicy.com/jobs?search_keywords=Model%20serving.md)[Agentic workflows](https://jobicy.com/jobs?search_keywords=Agentic%20workflows.md)[AI evaluation frameworks](https://jobicy.com/jobs?search_keywords=AI%20evaluation%20frameworks.md)[AI guardrails](https://jobicy.com/jobs?search_keywords=AI%20guardrails.md)[Retrieval-augmented generation](https://jobicy.com/jobs?search_keywords=Retrieval-augmented%20generation.md)[Vector databases](https://jobicy.com/jobs?search_keywords=Vector%20databases.md)[Python](https://jobicy.com/jobs?search_keywords=Python.md)[Java](https://jobicy.com/jobs?search_keywords=Java.md)[Go](https://jobicy.com/jobs?search_keywords=Go.md)[AWS](https://jobicy.com/jobs?search_keywords=AWS.md)[GCP](https://jobicy.com/jobs?search_keywords=GCP.md) Sample interview questionsHow would you design a production evaluation framework for an LLM-powered revenue workflow?I would define task-specific quality metrics, create representative offline datasets, and combine automated scoring with expert human review. I would version prompts, models, retrieval configurations, and datasets, then gate releases using regression thresholds. In production, I would monitor outcome quality, safety failures, latency, cost, and user feedback to continuously improve the evaluation set. How do you make an AI system reliable when the underlying model is non-deterministic? I design for bounded behavior rather than assuming deterministic output. This includes structured outputs, schema validation, retrieval grounding, confidence-aware routing, retries with limits, fallbacks, idempotent workflows, and human escalation for high-risk actions. Strong observability and evaluation-driven deployment are essential to detect regressions quickly. Describe an architecture for safely operating an autonomous AI agent in production. I would separate planning from execution, constrain available tools through least-privilege permissions, and validate every action against explicit policies. High-impact actions should require approval or deterministic verification, while all tool calls need audit logs, rate limits, and rollback or compensation paths. The agent should be evaluated in simulation and staged environments before expanded production access. How would you choose between self-hosted models and managed foundation-model APIs? I would evaluate quality, latency, total cost of ownership, data handling requirements, operational burden, customization needs, and vendor risk. Managed APIs are often best for fast iteration and broad capability, whereas self-hosting may be justified for strict data controls, predictable high-volume workloads, or specialized models. I would preserve an abstraction layer so the platform can benchmark and switch providers as models evolve. How do you lead technical direction across teams without direct management authority? I build alignment through clear technical narratives, prototypes, measurable trade-offs, and shared decision records. I involve product, data science, platform teams, and senior engineers early, invite rigorous challenge, and make outcomes visible through standards and reference implementations. Mentoring and enabling other teams to adopt the architecture are as important as making the original design decision. Opportunity details ## About this role. Job Title: Principal Software Engineer, AI Location: Remote – US Salesloft is building the next era of enterprise revenue — one where teams make confident decisions powered by AI and real signals. By combining our scale, insights, and AI innovation, we’re building the industry’s first Predictive Revenue System, enabling humans and AI to work together to make smarter decisions and drive consistent growth. With thousands of customers using our platforms every day, we have an unmatched view into how revenue is actually won — the Revenue Context that reveals what happens, when, and with what outcome. This gives us a unique opportunity to transform an entire category and set a new benchmark for how modern revenue teams operate. Join us to help transform how companies around the world run revenue — and build the platform that will guide leading revenue teams into the future. THE OPPORTUNITY At Salesloft, our Principal Engineer for AI sets the technical direction for how machine intelligence works across our Predictive Revenue System. This is the most senior individual-contributor role on our AI engineering team: you will architect the systems that turn LLMs, machine learning models, and agentic workflows into production capabilities that thousands of enterprise revenue teams depend on. This is deep, ambiguous, frontier work. You will make the foundational architectural decisions for AI at Salesloft — how we serve models at scale, how we build reliable systems on top of non-deterministic components, how we evaluate and guardrail agentic behavior, and how we keep pace as the underlying foundation models evolve. You will set the standards other engineers build on and act as a technical multiplier across the AI organization. On a day-to-day basis, you will: * Set the technical architecture and direction for AI systems across the platform — model serving, LLM-powered features, agentic workflows, and the infrastructure beneath them. * Design production-grade systems that are reliable and observable despite being built on non-deterministic, probabilistic AI components. * Architect evaluation frameworks, guardrails, and quality systems that make AI behavior safe, measurable, and trustworthy at enterprise scale. * Lead the most complex AI engineering problems end to end — from research-adjacent prototyping through hardened production delivery. * Establish engineering standards and best practices for building AI-native systems, and mentor senior engineers across the org. * Keep the platform at the frontier by evaluating and integrating advances in foundation models, agent frameworks, and the modern AI stack. This role lives at the heart of how we Lead From the Front: you’ll work at the edge of applied AI, prototype rapidly, and make architectural bets ahead of the field. And in the spirit of how we Make Your Mark, you’ll set the AI engineering standards and systems that the entire organization builds on — engineered for scale, not for a demo. WHAT WE’RE LOOKING FOR We are looking for an engineer who operates at the highest level of technical authority in AI systems — someone who has architected and shipped production AI at scale and understands the deep challenges of building reliable systems on probabilistic foundations. You lead through technical credibility and make the engineers around you better. You embody what we call Evidence Over Ego — you make architectural decisions from evaluation data and evidence rather than hype, you invite rigorous challenge, and you change direction when the models or the data move. You bring systems thinking to a genuinely unsolved problem space and build for durability. This is the right role if you want to define how AI works on a platform at the center of enterprise revenue. THE TEAM The Salesloft AI Engineering organization builds the machine intelligence at the core of the Predictive Revenue System. The team works at the intersection of applied research and production engineering, in close partnership with Product, Data Science, and Data Platform Engineering. The team Moves as One — AI engineering, product, and data science share ownership of shipping trustworthy AI and make hard technical calls together. And we are relentlessly Customer Inspired: every AI capability is measured against whether it makes revenue teams genuinely more successful and more confident in the system. THE SKILL SET * Extensive experience building and architecting production software systems at scale, with significant depth in AI/ML systems. * Hands-on experience building production systems on top of LLMs or machine learning models — including designing for the challenges of non-deterministic behavior. * Deep expertise in distributed systems, model serving, and the infrastructure required to run AI reliably at scale. * Proven experience designing evaluation frameworks, guardrails, or quality systems for AI-driven features. * A track record of setting technical architecture and standards across teams and mentoring senior engineers. * Strong command of a major programming language (e.g., Python, Java, or Go) and modern cloud infrastructure (AWS or GCP). * Hands-on experience with agentic systems — designing and hardening AI agents that take autonomous action in production. * Depth in the modern AI stack — retrieval-augmented generation, vector stores, agent frameworks, model orchestration, or evaluation tooling. BENEFITS: For more information about our company’s comprehensive benefits please visit: https://salesloftbenefits.com/ At Salesloft, we are committed to creating an inclusive and supportive workplace where everyone belongs and can thrive. We focus on culture add, not culture fit, and believe our teams are made stronger by the unique perspectives, experiences, and identities each person brings. We are proud to be an Equal Opportunity Employer and provide employment opportunities to all employees and applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, veteran status, pregnancy, or any other characteristic protected by law. If you’re excited about this role even though your experience may not perfectly match every requirement, we encourage you to apply. We are actively hiring across multiple geographies and would love to welcome passionate, curious, and mission-driven individuals to our growing team. Explore our open roles and consider joining us! Please note that all official communication regarding job opportunities at Salesloft will come from @ [salesloft.com](http://salesloft.com) email address. If you receive messages on LinkedIn or other job platforms claiming to be from Salesloft, they may not be legitimate. To verify the authenticity of any job-related communication, please visit our official [Careers Page](https://salesloft.com/company/careers/). We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, notetaking, or summarizing responses. These tools assist our recruitment team but do not replace human judgment – all hiring decisions are made by people. If you would like more information about how your data is processed or prefer to opt out of any AI-assisted tools, please let your recruiter know. Opting out will not impact your experience or consideration. #LI-REMOTE It is Salesloft’s intent to pay all Lofters competitive wages and salaries that are motivational, fair and equitable. The goal of Salesloft’s compensation program is to be transparent, attract potential employees, meet the needs of all current employees and encourage employees to stay with our organization. Actual compensation packages are based on several factors that are unique to each candidate, including but not limited to skill set, depth of experience, certifications, and specific work location. The total compensation package for this position may also include performance bonus, benefits and/or other applicable incentive compensation plans. 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