Interested in this AI/ML Engineer role at Asana?
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About This Role
We are looking for a Director of Engineering to lead our AI Platform organization. This group builds the foundational systems powering every AI experience across Asana. In this role, you will lead four key teams through their engineering managers: Context (search, retrieval, and knowledge extraction across the Asana Work Graph), LLM Foundations (model serving, inference infrastructure, provider strategy, and evaluation systems), and AI Efficiency (our center of excellence for cost, quality, and performance standards across all AI workloads). Collaborating with engineering managers and senior technical leaders, you will drive the end\-to\-end strategy, execution, and architecture that define how humans and AI work together at Asana to build trusted, reliable, high\-value product workflows for enterprise customers worldwide. Your mission is to make Asana's AI platform the most reliable, economical, and performant foundation in the industry for agentic enterprise software, giving Asana the leverage to ship AI products faster than anyone else.
This role is based in our San Francisco office with an office\-centric hybrid schedule. The standard in\-office days are Monday, Tuesday, and Thursday. Most Asanas have the option to work from home on Wednesdays. Working from home on Fridays depends on the type of work you do and the teams with which you partner. If you're interviewing for this role, your recruiter will share more about the in\-office requirements.
### What you'll achieve
- Drive Strategy \& Execution Across the AI Teammates Pillar: Lead the multi\-year vision and technical strategy for Asana's AI platform, covering retrieval and agent context, model serving and inference, model portfolio strategy, and evaluation systems.
- Optimize AI Infrastructure Costs: Own cost\-per\-execution as a primary engineering metric, managing model selection, routing, open\-weight versus frontier trade\-offs, inference optimization, caching, and prompt efficiency to protect product margins at scale.
- Establish AI Evaluation Frameworks: Establish evaluation frameworks, regression prevention, and performance standards that product teams can rely on out of the box, making quality, cost, and latency measurable defaults.
- Lead and Mentor Teams: Lead and mentor managers and their teams across San Francisco and New York City, building autonomous owners, recruiting top talent, and maintaining high execution standards.
- Manage Model Strategy: Evaluate when to adopt frontier models versus open\-weight models and structure provider relationships to prevent vendor lock\-in.
- Architectural Leadership: Pressure\-test architectural decisions, arbitrate build\-versus\-buy debates with data, and act as the trusted technical authority for AI infrastructure accuracy across the company.
- Drive Cross\-Functional Partnerships: Partner cross\-functionally with product, go\-to\-market, and finance leaders to tie platform investments directly to business performance, unit margins, and enterprise trust.
### About you
- 12\+ years of software engineering experience with 5\+ years of engineering leadership, including 2\+ years managing engineering managers. You've built organizations, not just teams: you've hired and developed managers, restructured when the mission changed, and held a bar for both.
- Proven track record of using AI tools daily, with deep expertise in serving architectures, inference providers, agentic frameworks, RAG architectures, and scale evaluation systems.
- Proven experience optimizing infrastructure costs and unit economics at scale with concrete metrics and clear trade\-offs.
- Platform mindset centered on earning trust, establishing clear standards, providing responsive support, and using data to guide team priorities.
- Skilled asynchronous communicator with experience leading distributed teams across time zones using strategy docs and decision memos.
- Adaptable decision\-maker who takes clear, evidence\-based positions but pivots quickly as new data emerges.
At Asana, we're committed to building teams that include a variety of backgrounds, perspectives, and skills, as this is critical to helping us achieve our mission. If you're interested in this role and don't meet every listed requirement, we still encourage you to apply.
### What we'll offer
Our comprehensive compensation package plays a big part in how we recognize you for the impact you have on our path to achieving our mission. We believe that compensation should be reflective of the value you create relative to the market value of your role. To ensure pay is fair and not impacted by biases, we're committed to looking at market value which is why we check ourselves and conduct a yearly pay equity audit.
For this role, the estimated base salary range is between $306,000\-$360,000\. The actual base salary will vary based on various factors, including market and individual qualifications objectively assessed during the interview process. The listed range above is a guideline, and the base salary range for this role may be modified.
In addition to base salary, your compensation package may include additional components such as equity, sales incentive pay (for most sales roles), and benefits. If you're interviewing for this role, speak with your recruiter to learn more about the total compensation and benefits for this role.
We strive to provide equitable and competitive benefits packages that support our employees worldwide and include:
- Mental health, wellness \& fitness benefits
- Career coaching \& support
- Inclusive family building benefits
- Long\-term savings or retirement plans
- In\-office culinary options to cater to your dietary preferences
These are just some of the benefits we offer, and benefits may vary based on role, country, and local regulations. If you're interviewing for this role, speak with your recruiter to learn more about the total compensation and benefits for this role.
\#LI\-Hybrid \#LI\-HC1
About us
Asana is a leading platform for human \+ AI collaboration. Millions of teams around the world rely on Asana to achieve their most important goals, faster. Asana has been named to Fortune's Best Workplaces for 7\+ years and recognized by Fast Company, Forbes, and Gartner for excellence in workplace culture and innovation. We offer an exceptional office\-centric culture while adopting the best elements of hybrid models to ensure that every one of our global team members can work together effortlessly. With 13\+ offices all over the world, we are always looking for individuals who care about building technology that drives positive change in the world and a culture where everyone feels that they belong.
Join Asana's Talent Network to stay up to date on job opportunities and life at Asana.
Salary Context
This $306K-$360K range is above the 75th percentile 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 Asana, 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. Director-level AI roles across all categories have a median of $274,554. This role's midpoint ($333K) sits 55% above the category median. Disclosed range: $306K to $360K.
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.
Asana AI Hiring
Asana has 3 open AI roles right now. They're hiring across AI Product Manager, AI Agent Developer, AI/ML Engineer. Based in San Francisco, CA, US. Compensation range: $230K - $360K.
Location Context
AI roles in San Francisco pay a median of $265,000 across 1,335 tracked positions. That's 23% 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 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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