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About This Role
Bitsight is a cyber risk management leader transforming how companies manage exposure, performance, and risk for themselves and their third parties. Companies rely on Bitsight to prioritize their cybersecurity investments, build greater trust within their ecosystem, and reduce their chances of financial loss.
Built on over a decade of technological innovation, its integrated solutions deliver value across enterprise security performance, digital supply chains, cyber insurance, and data analysis.
- We invented the cyber ratings industry in 2011
- Over 3000 customers trust Bitsight
- Over 750 teammates are dispersed throughout Boston, Raleigh, New York, Lisbon, Singapore, and remote
We are building an AI\-native Customer Success operating model from the ground up, and we’re looking for a hands\-on builder/operator to help bring it to life.
This role sits at the intersection of Customer Success, operations, AI, data, systems, and workflow design. The ideal candidate can take a clear business vision, rapidly prototype solutions, gather feedback, iterate quickly, and turn early ideas into scalable workflows that help the CS team operate with more speed, intelligence, and consistency.
This is not a traditional CS Ops administration role or a pure strategy role. This person will be expected to build. You’ll work closely with CS leadership to design and implement AI\-enabled workflows across lifecycle management, customer health, risk detection, renewal readiness, executive engagement, EBR/QBR preparation, digital engagement, support deflection, and CSM productivity.
The right person is technically curious, highly resourceful, comfortable with ambiguity, and genuinely excited by the opportunity to use AI to redesign how Customer Success work gets done.
What You’ll Do
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- Build AI\-powered workflows, automations, prompts, templates, and tools that reduce manual work and improve CS execution.
- Prototype and scale workflows such as automated EBR/QBR decks, renewal briefs, customer summaries, risk narratives, meeting prep, and next\-best\-action recommendations.
- Help operationalize the CS operating model, including lifecycle stages, health and risk models, renewal readiness, executive engagement, customer prioritization, and value tracking.
- Partner with CS, RevOps, Data, Support, Product, and other teams to connect the right data into the right workflows.
- Work across systems such as Salesforce, Gainsight, Clari, Gong, Zendesk, Snowflake, Slack, Zoom, Google Workspace, Claude, ChatGPT, and other AI/automation tools.
- Establish prompt structures, QA checks, feedback loops, and documentation to ensure AI outputs are useful, accurate, and scalable.
- Gather feedback from CSMs and leaders, iterate quickly, and continuously improve workflows based on real team usage.
- Help CSMs spend less time on manual reporting, prep, and coordination, and more time on high\-value customer engagement.
What Success Looks Like
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In the first 90 days, you will:
- Learn the CS operating model, current workflows, core systems, and biggest pain points.
- Identify the highest\-impact opportunities for AI and automation across the CS lifecycle.
- Ship several practical AI\-enabled workflows that reduce manual CSM effort.
- Build repeatable prompt, QA, and feedback processes.
- Help operationalize core lifecycle, health, risk, renewal, or EBR/QBR workflows.
- Demonstrate measurable impact on CSM productivity, consistency, or customer\-facing output quality.
Over time, you will help build a scalable AI and automation layer across the customer lifecycle and establish CS Ops as a strategic engine for scale, efficiency, and customer impact.
What We’re Looking For
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- 2–6\+ years of experience in Customer Success Operations, Revenue Operations, Support Operations, Technical Account Management, Solutions Consulting, Customer Success, Technical Support, or a similar role.
- Demonstrated experience using AI tools to build practical workflows, automations, internal tools, summaries, agents, or business processes.
- Strong builder/operator mindset with a bias toward fast prototyping and iteration.
- Comfort working with CRM, CS, support, revenue, or data systems.
- Ability to take loosely defined ideas and turn them into structured execution.
- Strong systems thinking across process, data, tools, and user experience.
- Comfortable operating in ambiguity and making pragmatic tradeoffs.
- Strong cross\-functional communication and documentation skills.
- High ownership, attention to detail, and willingness to incorporate feedback quickly.
Preferred Experience
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- Hands\-on experience with Salesforce, Gainsight, Gong, Clari, Zendesk, Slack, Zoom, Google Workspace, or similar GTM tools.
- Experience with Claude, Claude Code, ChatGPT, Gemini, Cursor, Replit, or other modern AI tools.
- Experience with automation tools such as Zapier, Make, Workato, n8n, Tray.io, or similar platforms.
- Familiarity with Snowflake, SQL, data warehouses, BI tools, or data modeling concepts.
- Experience building workflows that use CRM, call, support, product, or customer data.
- Customer\-facing experience in CS, Support, TAM, Solutions Consulting, or Technical Support.
- Basic scripting, API, or low\-code/no\-code automation experience.
- Experience administering or configuring GTM systems.
Belonging \& Inclusion. Bitsight is proud to be an equal opportunity employer. This means we do not tolerate discrimination of any kind and are committed to providing equal employment opportunities regardless of your gender identity, race, nationality, religion, sexual orientation, status as a protected veteran, or status as an individual with a disability.
Culture. We put our people first. Bitsight offers best in class benefits. We devote the same energy to nurturing our company's inclusive culture as we apply to serving our customers' needs. Working at Bitsight will give you the opportunity to fulfill your professional goals and expand your skills.
Open\-minded. If you got to this point, we hope you’re feeling excited about the job description you just read. Even if you don’t feel that you meet every single requirement, we still encourage you to apply. We’re eager to meet people that believe in Bitsight’s mission and can contribute to our team in a variety of ways.
Bitsight also provides reasonable accommodations to qualified individuals with disabilities or based on a sincerely held religious belief in accordance with applicable laws. If you need to inquire about a reasonable accommodation, or need assistance with completing the application process, please email recruiting@bitsight.com. This contact information is for accommodation requests only, and cannot be used to inquire about the status of applications.
Additional Information for United States of America Applicants:
Bitsight is committed to compliance with all fair employment practices regarding citizenship and immigration status.
Bitsight will not discharge, discipline or in any other manner discriminate against any employee or applicant for employment because such employee or applicant has inquired about, discussed, or disclosed the compensation of the employee or applicant or another employee or applicant.
Massachusetts Applicants: *It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability.*
Qualified applicants with criminal histories will be considered for employment consistent with applicable law.
This position may be considered a promotional opportunity pursuant to the Colorado Equal Pay for Equal Work Act.
The anticipated hiring base salary range for this position is US$78,980 to $117,000 annually for US\-based employees. This range reflects the minimum and maximum target for new hire salaries for the position across all US locations, is based on a full\-time work schedule, and is Bitsight’s good faith estimate as of the date of this posting. Within the range, individual pay is determined by work location and additional factors, including job\-related skills, experience, and relevant education or training.
In addition to base salary, this role is eligible for participation in a bonus or commission plan and an equity grant. Bitsight also offers a competitive benefits package, including but not but limited to medical, dental, and vision insurance; paid parental leave; flexible time off; a 401(k) plan with employee and company contribution opportunities; life and disability insurance; and tuition reimbursement.
Salary Context
This $78K-$117K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1841 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Bitsight, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($97K) sits 55% below the category median. Disclosed range: $78K to $117K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Bitsight AI Hiring
Bitsight has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Boston, MA, US. Compensation range: $117K - $117K.
Location Context
AI roles in Boston pay a median of $210,000 across 97 tracked positions. That's 3% below the national median.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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