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About This Role
Company Overview Spectrix Analytical Services, LLC is a dedicated provider of on-site analytical chemistry services, delivering reliable and high-quality results since 1999. Our team of experienced scientists supports research and development efforts across various industries by generating precise analytical data and maintaining sophisticated instrumentation directly at our clients' laboratories.
Spectrix Analytical Services is seeking a ML Platform / Agentic AI Engineer. This contract role focuses on building and operating the compute platform, MLOps, and agentic tooling that support protein therapeutic design. This is a full-time (40 hours/week) remote position open to candidates in the United States or Canada. Working hours within one to two hours of US Eastern time are preferred; West Coast candidates who can overlap with Eastern business hours will also be considered.
The contractor will work across a Kubernetes-based workflow orchestration platform, a scientific tools monorepo of containerized computational biology applications, and the data and model infrastructure that connects them to downstream analytics in Snowflake. Depending on background, work will center on one or more of the areas below; we expect contractors to work across area boundaries as priorities shift.
Areas of work:
- Platform and workflow engineering. Extend the Kubernetes orchestration platform and the workflows that run on it, including migrating existing application backends onto it. Make the platform region-agnostic so compute can be scheduled in new AWS regions and accounts with configuration rather than code changes.
- MLOps and model serving. Build the registration, versioning, and serving path for models so that model outputs are available to scientific applications and to downstream reporting. Integrate model output with the data layer rather than leaving it in per-project artifacts.
- Automated benchmarking. Build automated benchmarking and retraining for computational tools, LLM models, and agent skills used in de novo design. This includes GPU-backed benchmarking of larger models, test-set and dataset tracking, and a persistent record of benchmark results over time.
- Agentic AI integration. Incorporate agentic AI into existing scientific applications: tool and skill definitions, evaluation harnesses, and the plumbing that lets agents call internal services safely.
- Data engineering. Build reliable S3-to-Snowflake data pulls and automated registration of de novo design results, with monitoring and backfill behavior that does not require manual intervention.
- Performance optimization. Take existing production pipelines and reduce their runtime and cost — profiling, GPU utilization, batching, scheduling, and container image work.
Key Responsibilities:
- Develop and maintain production Python services, pipelines, and CLI tooling.
- Build and maintain Helm charts, container images, and deployment infrastructure as part of daily workflow.
- Deploy and operate workloads on Kubernetes across multiple AWS regions and accounts.
- Instrument pipelines and models so that performance, cost, and benchmark results are measurable.
- Work directly with scientists and engineering leads to decide what to build, and support adoption through documentation and hands-on help.
Qualifications:
- Bachelor’s degree with 7–10 years of relevant experience, or a Master’s degree with 5 or more years of relevant experience.
- Strong Python development skills with experience building production-grade tooling or pipelines.
- Working knowledge of Kubernetes, Helm, and container-based deployments.
- Experience developing, deploying, or optimizing machine learning pipelines in a production environment.
- Familiarity with infrastructure-as-code and CI/CD practices.
- Experience with AWS, including multi-region or multi-account deployments.
- Experience with data warehousing and ELT patterns, Snowflake preferred.
- Experience on a small team (roughly 3–10 engineers) where individuals own systems end to end, set their own priorities within a goal, and ship without a dedicated QA, SRE, or release function.
- Routine use of agentic coding harnesses — Claude Code, OpenCode, Codex, or equivalent — as part of day-to-day development, with a view on where they help and where they do not.
- Exposure to Rust is a plus.
- Experience building LLM agents, tool definitions, or evaluation harnesses is a plus.
- Willingness to learn computational biology and protein science domains.
- Effective communicator who can work independently in a remote environment and collaborate with both engineers and scientists.
Spectrix offers flexible schedules, competitive salaries, and a committed work environment to its employees; the company provides automatic benefits of life insurance and short and long term disability insurance. Optional benefits for full time employees include health, dental, and vision insurance, college loan assistance, and an employer matched retirement savings plan. Spectrix is not a placement agency.
Spectrix is participating in the E-Verify program of the U.S. Department of Homeland Security (phone number: 888-897-7781, and website: www.dhs.gov/E-Verify).
Pay: $48.00 - $58.00 per hour
Expected hours: 40.0 per week
Work Location: Remote
Salary Context
This $99K-$120K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Spectrix Analytical Services, LLC, 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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($110K) sits 48% below the category median. Disclosed range: $99K to $120K.
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.
Spectrix Analytical Services, LLC AI Hiring
Spectrix Analytical Services, LLC has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $120K - $120K.
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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