Interested in this AI/ML Engineer role at Keep Company?
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Brass Tacks
- Remote - ideally East Coast. Able and willing to travel to our Bethesda HQ monthly.
- $140K base
- Meaningful equity in our early-stage, venture-backed company
- We pay 100% of the premium for two UHC Gold plans and 50% for your dependents. Same approach for dental and vision
- 401(k) + FSA + HSA + Commuter benefits
- High accountability + high flexibility culture
About Keep Company
Keep Company is on a mission to make human connection an intentional, scalable asset in every workplace. As a venture-backed SaaS platform recognized by Techstars, Forbes, and SHRM, Keep Company blends technology with coaching and mentoring to deliver transformative, measurable employee experiences at leading law and professional services firms. We help organizations accelerate culture, retention, and productivity, especially in moments that matter most.
Keep Company is the platform for people programs, helping professional services firms build and scale the relationships that develop their people and keep top talent.
As AI transforms how work gets done, the relationships and human skills that build judgment, confidence, and performance are increasingly important to how firms develop their people and differentiate themselves. Keep Company helps firms create those relationships by design — across career stages and seasons — so they can develop their people, keep the talent who set them apart, and stay ahead as work changes.
Our configurable technology, embedded expertise, and actionable insights make it easier to build, manage, measure, and scale relationship-based programs across the firm. Organizations can use Keep Company’s platform to power their own programs — from mentoring and group coaching to peer groups and ERGs — or launch full-service programs designed and live-facilitated by our team. We combine technology that makes development easier to scale with human expertise where it matters most.
The Role
This is Keep Company’s first dedicated data hire. It is a hands-on build role: initially weighted toward data engineering and analytics infrastructure, with meaningful ownership of model development, evaluation, and strategy as the foundation matures.
You will own how data moves through Keep Company: from source systems and product events to clean, trusted, member-centric datasets, reporting, and early predictive use cases.
You will partner closely with Product and Engineering to define the signals we should capture, build the infrastructure that makes those signals usable, and evaluate where models can improve matching, recommendations, and intervention decisions.
The first phase of the role will be primarily focused on the foundations: pipelines, data modeling, instrumentation, data quality, and reusable reporting. Over time, you will lead the strategy and development of models that help Keep Company identify risks, improve relationship outcomes, and make more useful recommendations.
What You'll Own
Data engineering and foundations
- Own Keep Company’s analytical data foundation, including the warehouse, transformations, pipelines, data quality, and documentation.
- Establish MotherDuck as a reliable analytical source of truth, including dbt models, source definitions, and durable datasets for internal and in-product use.
- Partner with Engineering to improve how product, program, survey, HRIS, import, and coach data is collected, structured, and maintained.
- Build and maintain pipelines across current inputs, including WorkOS/HRIS integrations, CSV imports, surveys, forms, and platform activity.
- Bring historical data into a clean, member-centric structure that connects attendance, registrations, survey responses, coaching inputs, relationship activity, and outcomes.
- Define and maintain a shared taxonomy for constructs, questions, scales, programs, cohorts, and other historical data that is currently inconsistent.
- Partner with Product and Engineering to define product instrumentation: what actions, milestones, decisions, and outcomes should be captured, where they should live, and how they can be used.
Reporting, measurement, and insight
- Build reusable measurement layers and reporting products rather than one-off analyses.
- Develop reporting that helps clients understand relationship participation, engagement, matching quality, and program outcomes.
- Establish an internal operating cadence for data: trusted dashboards and recurring analysis that leadership uses to make decisions.
- Partner with Product, Programs, Client Success, and leadership to define meaningful metrics and distinguish signals from noise.
- Build analysis that tests and improves Keep Company’s core assumptions, including how relationship design and matching affect engagement, retention, and development.
Model development and data strategy
- Own the strategy for moving from descriptive reporting toward predictive and recommendation capabilities.
- Document, evaluate, and improve the matching logic currently used in the product, including its assumptions, objective functions, scoring logic, failure modes, and quality tradeoffs.
- Build and test early models, heuristics, and prototypes that improve matching, identify risk, or support more useful recommendations.
- Define what additional data is required to support future prediction and recommendation use cases—and prioritize the collection and instrumentation needed to obtain it.
- Develop an experimentation approach for model-backed features: establish baselines, define success criteria, monitor quality, and improve systems over time.
- Identify where a heuristic is sufficient and where a more rigorous model is warranted.
- Build toward future use cases such as identifying low-quality matches before launch, detecting disengagement or at-risk programs early, and recommending the appropriate relationship type for a given employee lifecycle moment.
- Ensure predictive capabilities can be introduced thoughtfully, including privacy protections, tenant isolation, explainability, confidence thresholds, and paths for clients with AI restrictions.
Who You Are
Must Have
- 4-6 years of strong hands-on experience in data engineering, product analytics, decision science and/or data science, at a technology company with a track record of influencing product decisions.
- Experience with transformation tooling such as dbt, and with building reliable ingestion and transformation pipelines.
- Deep SQL fluency and experience designing and maintaining warehouse data models.
- Experience working with messy, incomplete, inconsistent, or historically unstandardized data.
- Strong background designing well-powered A/B tests, diagnosing bias and variance issues, and interpreting results under real-world conditions.
- Ability to translate business and product questions into durable datasets, metrics, reporting, and decision-making tools.
- Experience partnering with Product and Engineering to define instrumentation and turn product behavior into usable data.
- Working knowledge of statistics, experimentation, clustering, matching, and predictive modeling—enough to build or evaluate early models and set a sound modeling roadmap.
- Good judgment about data quality, privacy, sensitive attributes, tenant isolation, and consent-based product design.
- Strong communication skills and comfort explaining data, tradeoffs, uncertainty, and model behavior to non-technical stakeholders.
Preferred
- Experience with MotherDuck, DuckDB, Aurora/Postgres-derived data, S3, Parquet, WorkOS, Hex, or similar tools.
- Experience building recommendation, matching, prioritization, or prediction systems.
- Experience evaluating or improving a heuristic-based system before moving to more formal machine-learning approaches.
- Experience working alongside ML engineers on production models, including offline evaluation, online metrics, and guardrails.
- Experience building data products or analytics capabilities in a B2B SaaS environment.
- Experience with employee, engagement, professional-services, people, or relationship data.
- Experience using modern AI tools or open-weight models responsibly in product or data workflows.
- Experience helping build or lead a growing data, analytics, or BI function.
What Success Looks Like
In the first 6–12 months, you will have:
- Created a trusted, documented analytical foundation for Keep Company’s core data.
- Improved the reliability and usability of historical member, program, survey, and coaching data.
- Defined the most important product and outcome signals Keep Company needs to capture.
- Delivered reusable internal and client-facing reporting built on durable datasets.
- Evaluated the current matching approach and established a clear roadmap for model development.
- Tested at least one early predictive or recommendation-oriented use case with appropriate measurement and safeguards.
- Made data a consistent input into product, client, and leadership decisions.
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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Keep Company, 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 in Demand for This Role
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 $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Keep Company AI Hiring
Keep Company has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $188,000 across 2,316 positions. About 6% of all AI roles offer remote work.
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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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