Interested in this AI/ML Engineer role at Tango Technology, Inc.?
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About This Role
Let’s Tango! Where Innovation Meets Impact.
At Tango Analytics, we’re all about helping businesses make smarter decisions through powerful technology, insightful data, and a whole lot of collaboration. Whether you're a creative thinker, a strategic planner, a tech wizard, or a customer champion, there's a place for you on our team. We believe work should be meaningful *and* fun — so if you're ready to make a difference while enjoying the journey, come join us and let's Tango!
Role Summary:
We are looking for a Senior Applied AI Engineer to help build and ship Tango’s first AI\-powered product, marking an important next chapter after 18 years as an industry leader in real estate technology.
In this highly hands\-on role, you will turn advances in generative AI and machine learning into reliable, production\-ready capabilities that solve meaningful problems for our customers. You’ll work closely with Product, Engineering, and domain experts to identify high\-value use cases, rapidly prototype solutions, evaluate approaches, and take the best ideas all the way into production.
This is an opportunity to help establish how AI is built and applied at Tango from the ground up. You’ll influence technical architecture, engineering standards, evaluation practices, and product strategy while tackling complex, data\-rich workflows across corporate real estate, workplace management, lease administration, and retail location management.
Key Responsibilities:
Agent Design \& Delivery
- Design, build, and ship production AI agents on LangGraph, keeping the agent layer portable across cloud platforms.
- Own agents end to end: graph design, tool definitions, prompt and context engineering, durable execution, failure and retry behavior, and cost and latency budgets.
- Build agent tooling against internal systems over MCP, using direct API calls where those are the better fit, and agent\-to\-agent interfaces as agents begin to compose.
- Deliver human\-in\-the\-loop review flows, including interrupt points, confidence surfacing, and the correction paths that let a customer review and override agent output.
- Build and tune retrieval, covering chunking, hybrid retrieval, grounding, and citation back to source page and paragraph.
Evaluation, Quality \& Trust
- Contribute the evaluations for the agents you ship: golden datasets, LLM\-as\-judge and deterministic scorers, and regression suites that run in CI, built against the shared evaluation harness the platform provides.
- Diagnose quality failures to root cause, whether retrieval miss, prompt defect, tool error, model regression, or flawed ground truth, and correct the appropriate layer.
- Own agent\-level safety behavior, including prompt\-injection resistance, PII handling, and refusal and escalation paths, applying the platform guardrail service maintained by Platform Engineering.
Cross\-Functional Collaboration
- Partner with Product to translate accuracy thresholds, confidence disclosure, and human\-in\-the\-loop triggers into shipped behavior.
- Work with Platform Engineering on deployment, and with Data Platform on the curated datasets agents read.
- Feed curated agent session and usage analytics into the warehouse so agent performance is measurable alongside product analytics.
- Transition reference agents to the domain teams that will operate them long\-term, and contribute to the shared agent quality standard.
Required Skills:
- 7\+ years of professional software engineering experience, including 2\+ years building LLM\-powered systems that reached production and real users.
- Strong expertise with Python and its service stack (FastAPI, Pydantic, or equivalents), along with the engineering discipline that supports it: testing, code review, CI/CD, and production ownership.
- Production experience with an agent orchestration framework. LangGraph strongly preferred; LangChain, OpenAI or Claude Agents SDKs, or equivalent frameworks considered.
- Hands\-on depth with at least one frontier model API.
- Hands\-on experience with LLM evaluation: golden datasets, LLM\-as\-judge and deterministic scorers, regression testing, and using evaluation results to gate releases. This is central to the role.
- Experience with MCP tool servers or comparable tool and function\-calling protocols, and with multi\-agent patterns.
- Production RAG and retrieval experience, including chunking strategy, hybrid retrieval, grounding, citation, and diagnosing retrieval failures.
- Experience with LLM observability and tracing (LangSmith, Langfuse, Arize, or equivalent), and with prompt and version management.
- Sound judgment about failure modes such as hallucination, prompt injection, and silent degradation, with the ability to distinguish acceptable failures from unacceptable ones.
Preferred:
- Graph\-backed agent memory or knowledge graphs (Neo4j or similar).
- Production vector and hybrid retrieval stores (pgvector, Pinecone, Weaviate, Qdrant).
- Async task orchestration for long\-running document pipelines (Celery/Redis or equivalent).
- Document intelligence and information extraction at scale, including OCR, layout\-aware parsing, and structured extraction from long documents.
What We Offer
We’re committed to creating an environment where you can thrive—professionally and personally. Our offerings include:
- Competitive Compensation We recognize and reward your contributions with a salary package that reflects your value.
- Comprehensive Benefits Including health, dental, and vision insurance, a 401(k) plan with company match, and generous paid time off to support your well\-being.
- Flexible Work Environment Whether remote, hybrid, or in\-office, we support work arrangements that promote productivity and balance.
- Inclusive \& Collaborative Culture We foster a workplace where diverse perspectives are valued, teamwork is encouraged, and everyone has a voice.
Tango is proud to be an equal opportunity employer. We are committed to equal opportunity regardless of race, ethnicity, religion, parental status, sexual orientation, age, citizenship, disability, or veteran status.
Base pay offered is contingent on qualifications and other operational considerations. Base pay is just one piece of the full compensation structure offered at Tango. If this pay range is outside of your expectations, we still encourage you to apply and have a conversation with us.
Base pay offered for this position is: $160,000 \- $190,000
- Applicants must be authorized to work in the U.S. for any employer.
- We cannot sponsor employment\-based visas at this time.
Salary Context
This $160K-$190K range is above the median 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 Tango Technology, Inc., 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 ($175K) sits 19% below the category median. Disclosed range: $160K to $190K.
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.
Tango Technology, Inc. AI Hiring
Tango Technology, Inc. has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in OR, US. Compensation range: $190K - $190K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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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