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About This Role
At TWG Group Holdings, LLC (“TWG Global”), we drive innovation and business transformation across a range of industries—including financial services, insurance, technology, media, and sports—by leveraging data and AI as core assets. Our AI\-first, cloud\-native approach delivers real\-time intelligence and interactive business applications, empowering informed decision\-making for both customers and employees.
We prioritize responsible data and AI practices, ensuring ethical standards and regulatory compliance. Our decentralized structure enables each business unit to operate autonomously, supported by a central AI Solutions Group, while strategic partnerships with leading data and AI vendors fuel game\-changing efforts in marketing, operations, and product development.
You will collaborate with management to advance our data and analytics transformation, enhance productivity, and enable agile, data\-driven decisions. By leveraging relationships with top tech startups and universities, you will help create competitive advantages and drive enterprise innovation.
At TWG Global, your contributions will support our goal of sustained growth and superior returns, as we deliver rare value and impact across our businesses.
The Role
TWG Global is seeking a Senior or Staff AI Software Engineer in Test to join our AI Engineering team building commercial\-grade AI products. This is a software engineering role focused on test automation. You won’t just write test cases, you’ll design and build the frameworks, harnesses, evaluation infrastructure, and tooling that make testing AI agents and LLM\-powered applications possible at scale.
Our agents are written in LangGraph and run on Azure on the TWG side, with a parallel Vercel\-based stack on the Palantir side. You’ll write eval sets against both, and you’ll validate the surfaces our users actually touch: iOS apps, plugins, and Chrome extensions, not just the model layer.
You’ll work shoulder\-to\-shoulder with AI engineers and data scientists, contributing production\-quality code to shared repositories. The ideal candidate is a strong coder, fluent in Python and Java — who has shipped automated test infrastructure in a production environment and has hands\-on experience evaluating LLM and agentic systems.
Key Responsibilities
*Framework and harness engineering*
- Design and build scalable, reusable test automation frameworks for AI agents, LLM\-powered applications, and underlying APIs.
- Write clean, maintainable Python for test harnesses, eval pipelines, synthetic data generation utilities, and internal tooling.
- Treat test code as production code: code review, type hints, documentation, library design.
*Evaluation infrastructure*
- Build evaluation infrastructure for benchmarking agent performance against SOTA LLMs, competitors, and internal baselines.
- Own regression suites, golden datasets, rubric\-based evals, and metric dashboards.
- Build tooling for synthetic test data generation, edge\-case discovery, and adversarial testing.
*Resilience and load*
- Design and run release, system, performance, and load tests against streaming, stateful, and async systems.
- Build chaos and fault injection tooling for token expiry, connection pool exhaustion, provider failover, and cache pressure scenarios.
- Drive contract testing across LLM providers (Bedrock, Anthropic, OpenAI) to catch parity drift.
*CI/CD and observability*
- Integrate automated tests into CI/CD so every model, prompt, and code change is validated before it ships.
- Build trace\-based assertions on LangGraph state, tool calls, and agent decisions — debugging an agent failure means replaying graph state, not re\-running a prompt.
- Make observability a first\-class testing surface (LangSmith, audit logs).
*Human\-in\-the\-loop and partnership*
- Implement HIL review workflows where automation alone cannot validate quality, then push the automation boundary outward.
- Partner with AI engineers and data scientists on model evaluation, training and eval data prep, and root\-cause debugging of complex end\-to\-end failures.
- Champion quality engineering practices across the team: code review, coverage standards, observability, reproducibility.
- Ensure user\-centric validation so AI outputs are accurate, reliable, and meet real\-world application needs.
Requirements
- 3–7 years of software engineering experience, with a meaningful portion focused on test automation, SDET, or software engineering in test roles.
- Expert\-level Python. You write Python every day, design libraries other engineers use, and apply OOP and clean\-code practices.
- Hands\-on Java experience, enough to read, write, and test Java services, not just touch them.
- Working understanding of the LangGraph or Vercel frameworks: graph state, nodes, edges, tool calls, and how to write evals against agentic flows.
- Demonstrated experience building eval sets for LLM models (this is critical to the role).
- Experience testing across multiple client surfaces: iOS apps, plugins, and Chrome extensions.
- Hands\-on experience building automated test suites with frameworks such as pytest, Selenium, Playwright, Cypress, or similar.
- Proven experience integrating test automation into CI/CD systems (GitHub Actions, Jenkins, CircleCI, GitLab CI, or similar).
- Strong skills in data manipulation, test data preparation, and SQL.
- Bachelor’s degree or higher in Computer Science, Engineering, or a related field.
Strongly preferred:
- Experience with Azure (our primary cloud) and containerization (Docker).
- Experience testing RAG pipelines, agentic workflows, or multi\-step tool\-calling systems.
Benefits Position Location:
This position is located in Santa Monica, CA (on\-site).
Compensation:
The base pay for this position is $190,000\-250,000\. A bonus will be provided as part of the compensation package, in addition to a full range of medical, financial, and/or other benefits.
*TWG is an equal opportunity employer, and all qualified applicants will receive consideration for employment without regard to race, color, religion, gender, sexual orientation, gender identity, national origin, disability, or status as a protected veteran.*
Salary Context
This $160K-$190K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At TWG Global AI, 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 $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. Disclosed range: $160K to $190K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
TWG Global AI AI Hiring
TWG Global AI has 2 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer. Based in New York, NY, US. Compensation range: $190K - $200K.
Location Context
AI roles in New York pay a median of $211,000 across 2,643 tracked positions. That's 5% above 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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