Interested in this AI/ML Engineer role at Calix?
Apply Now →Skills & Technologies
About This Role
The Calix platform enables Communication Service Providers (CSPs) of all sizes to transform and future\-proof their businesses. Through real\-time data, automation, and actionable insights delivered via Calix One — our cloud\-first, AI\-powered platform — CSPs can simplify operations, collapse cost, and accelerate innovation. Calix One brings together the automation of everything and the experience of one, empowering customers to deliver differentiated subscriber experiences while driving acquisition, loyalty, and revenue growth. This is the Calix mission: to enable CSPs of all sizes to simplify, innovate, and grow, strengthening both their businesses and the communities they serve.
We’re at the forefront of a once in a generational change in the broadband industry. Join us as we innovate, help our customers reach their potential, and connect underserved communities with unrivaled digital experiences.
We are standing up an enterprise AI capability across our product organization, and we are looking for a seasoned technical lead to drive it end to end. This is a hands\-on role: you will set the technical direction, architecture, and standards, mentor engineers, and steer external delivery partners \- while still writing code, building reference implementations, and personally unblocking the hardest problems.
This is a remote\-based position located in the United States or Canada. Please note that as part of the recruitment and hiring process, there is an in\-person meeting that will take place*.*
What You'll Do
Enterprise platform rollout \& enablement
- Own the architecture and rollout of the enterprise AI platform across the product organization.
- Build and operate cost\-effective infrastructure: model garden setup, budget controls and cost monitoring, model tiering, and fallback open\-source models.
- Stand up and harden platform foundations \- identity federation/SSO, IAM, tenant isolation, security and compliance guardrails, and observability.
- Enable users to adopt off\-the\-shelf capabilities (e.g., NotebookLM, enterprise search, code assistants) and provide the training and ongoing support that drives real adoption.
- Design and deliver a self\-service “agent factory” \- patterns, templates, and in\-IDE guardrails \- so teams can build and maintain their own agents safely.
Product development lifecycle agents
- Lead the design and build of agent workflows across the PDLC:
- Ideation:requirement ideation and generation, design specification generation.
- Development:coding assistants, source\-code management.
- Deployment:CI/CD, deployment, and monitoring.
- Partner directly with product, engineering, and other business teams to understand their requirements and help build out their use\-cases \- not just ship infrastructure but drive measurable productivity gains.
- Establish evaluation, quality, and monitoring practices for agent workflows from sandbox to production.
Cross\-platform interoperability
- Build agents that interoperate with agents on other enterprise AI platforms, including cross\-platform contracts, schema/intent mapping, and cross\-perimeter authentication.
- Represent the product organization technically when working with other business teams on cross\-platform agent designs.
Across all tracks\-leadership \& hands\-on
- Own the technical strategy, standards, patterns, and guardrails for agentic workflow development, RAG, security, and governance.
- Lead and mentor a team across cloud and enterprise AI tracks; raise the bar through code review, pairing, and design reviews.
- Steer external delivery partners \- scope work, review deliverables, and hold them to quality and timeline.
- Stay hands\-on: build reference agents, RAG pipelines, integrations, and MCP connectors, and debug the hardest pieces (including non\-deterministic retrieval/generation behavior).
Required Qualifications
- 10\+ years of software engineering experience, with 4\+ years in a technical lead or staff\-level role.
- Strong, current hands\-on coding ability \- you still build and ship. Proficiency in Python (and comfort across at least one other modern language).
- Experience designing and operating production AI/ML or LLM\-based systems: agents, RAG, prompt/eval pipelines, or similar.
- Deep familiarity with Google Vertex AI / Gemini and the surrounding services (IAM, networking, observability, cost management).
- Experience building developer\-facing platforms or internal tooling, and driving adoption with training and support.
- Experience integrating systems via APIs and connectors; comfort with authentication and identity federation (OAuth, SSO, workload identity).
- A track record of leading technical initiatives across teams and influencing without authority.
- Strong communication skills \- able to work directly with both engineers and non\-technical business stakeholders.
Preferred Qualifications
- Hands\-on with Gemini Enterprise, Vertex AI, NotebookLM, or comparable enterprise AI tooling.
- Experience building agentic systems and orchestration (agent development kits, A2A protocols, MCP, tool/function calling).
- Experience with LLM gateways and routing (e.g., LiteLLM), model cost optimization, and multi\-model fallback strategies.
- Experience managing or working alongside systems integrators / delivery partners.
- Familiarity with the modern PDLC toolchain (Jira/Confluence, Figma, GitHub, CI/CD) and AI coding assistants.
- Experience with AI governance, guardrails, data privacy, and compliance in an enterprise setting.
\#LI\-Remote
The base pay range for this position varies based on the geographic location. More information about the pay range specific to candidate location and other factors will be shared during the recruitment process. Individual pay is determined based on location of residence and multiple factors, including job\-related knowledge, skills and experience.
San Francisco Bay Area:
156,400 \- 265,700 USD AnnualAll Other US Locations:
136,000 \- 231,000 USD Annual
As a part of the total compensation package, this role may be eligible for a bonus. For information on our benefits click here.
### About Us
PLEASE NOTE: All emails from Calix will come from a '@calix.com' email address. Please verify and confirm any communication from Calix prior to disclosing any personal or financial information. If you receive a communication that you think may not be from Calix, please report it to us at talentandculture@calix.com.
The Calix platform enables Communication Service Providers (CSPs) of all sizes to transform and future\-proof their businesses. Through real\-time data, automation, and actionable insights delivered via Calix One — our cloud\-first, AI\-powered platform — CSPs can simplify operations, collapse cost, and accelerate innovation. Calix One brings together the automation of everything and the experience of one, empowering customers to deliver differentiated subscriber experiences while driving acquisition, loyalty, and revenue growth. This is the Calix mission: to enable CSPs of all sizes to simplify, innovate, and grow, strengthening both their businesses and the communities they serve.
We’re at the forefront of a once‑in‑a‑generation change in the broadband industry. Join us as we innovate, help our customers reach their potential, and connect underserved communities with unrivaled digital experiences.
If you are a person with a disability needing assistance with the application process please:
- Email us at calix.interview@calix.com; or
- Call us at \+1 (408\) 514\-3000\.
Calix is a Drug Free Workplace.
Salary Context
This $156K-$265K 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 Calix, 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. Disclosed range: $156K to $265K.
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.
Calix AI Hiring
Calix has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $265K - $265K.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% 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 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.