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About This Role
About Parallel Works
Parallel Works builds and operates ACTIVATE, a control plane for high performance computing and AI. Our customers run large scientific and AI workloads across their own on\-premises clusters, Government and commercial cloud, and commercial GPU providers, and ACTIVATE gives them one way in to all of it. The high security boundary is authorized at Impact Level 5, with FIPS validated cryptography and STIG hardening throughout.
The work reaches most fields that depend on computing at scale: weather and climate forecasting, defense and intelligence programs, aerospace and structural analysis, molecular and materials science, energy, and AI research. A quarter here can include standing up a GPU cluster for one of those communities, federating a laboratory's existing on\-premises system with burst capacity it did not have before, and getting a domain code written decades ago to run on current hardware.
Customer success sets our priorities. We are a small engineering company, so engineers here work directly with the people using the systems and carry a problem from the first report through to the fix. This is what we call mission engineering: understanding what a customer is trying to accomplish and why the computing matters to it.
About the role
Parallel Works is hiring an AI Compute Sales Lead to grow the managed GPU business. We run managed GPU clusters where a provider supplies the hardware and ACTIVATE is the control plane and support layer on top. In each case the customer wanted usable GPU capacity without building a cluster operations team of its own.
The market is research institutions, Federal laboratories, AI companies, and commercial research and development groups. Parallel Works is provider agnostic and federates on\-premises GPU clusters, NeoCloud capacity, and hyperscaler regions under one control plane, so the product sold is the operating layer and the support behind it. Many of these customers already own GPUs on site and want burst capacity that does not split their user base. There is no established pipeline in this segment yet, so building one is the first task.
What you will do* Generate pipeline: build the funnel from a standing start through outbound, research community networks, conferences, and partner channels.
- Sell with providers: run co\-selling motions with GPU providers and hyperscalers, where the provider brings capacity and we bring the managed platform.
- Technical discovery: qualify a workload well enough to scope it: cluster size, GPU generation, interconnect, storage throughput, framework and job pattern, Slurm or Kubernetes, and what the customer already owns.
- Hybrid deals: combine a customer's existing on\-premises GPU cluster with reserved NeoCloud capacity and hyperscaler burst under one control plane. These are the largest engagements in the segment.
- Commercial case: work the GPU economics: owned hardware against rented capacity, reserved against on demand, price per GPU hour, realistic utilization, and chargeback across research groups.
- Run the deal: manage multi\-stakeholder cycles across research leadership, central IT, security, procurement, and finance.
Requirements
- 5 or more years selling infrastructure, cloud, GPU capacity, or AI platform software, with quota attainment you can describe.
- GPU capacity economics: how on demand, reserved, and committed capacity differ commercially, and how a customer's own cluster compares to rented capacity once utilization and operations staff are counted.
- Leading a technical discovery conversation about training and inference workloads without an engineer present.
- Partner led or co\-selling motions.
- Generating your own pipeline instead of working inbound leads.
You do not need every item on this list. If you have most of it and work well with other people, apply.
Preferred Qualifications* NeoCloud or GPU as a service market experience.
- Sales into research institutions, national laboratories, university research computing, or AI labs.
- Familiarity with Slurm and Kubernetes as delivery models.
- Federal or public sector exposure, including how those procurement timelines differ from commercial ones.
- Time at an early stage or small company, where the seller also carries qualification and follow through.
Benefits
Medical, vision, and dental coverage, a 401(k) with company match, short term disability, and generous paid vacation and sick time.
The role also carries sales commission.
Equal employment opportunity
Parallel Works is an equal opportunity employer. We consider all qualified applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, protected veteran status, or any other characteristic protected by law.
Salary Context
This $90K-$120K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Parallel Works, 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($105K) sits 51% below the category median. Disclosed range: $90K to $120K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Parallel Works AI Hiring
Parallel Works has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Chicago, IL, US. Compensation range: $120K - $120K.
Location Context
AI roles in Chicago pay a median of $192,900 across 197 tracked positions. That's 10% 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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