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About This Role
Your wellbeing, our mission. Join a company shaping a healthier world.
GET TO KNOW US
At Wellhub we're revolutionizing workplace wellness. Our platform connects employees worldwide to the best partners for fitness, mindfulness, therapy, nutrition, and sleep—all in one simple subscription. Headquartered in NYC with team members in Europe, North America and South America, we're on a mission to make every company a wellness company.
We believe work should be fulfilling, inspiring, and balanced. Here, you'll find a team that values wellbeing, collaboration, and different perspectives, where passion and creativity push boundaries to create real impact. Your contributions will help shape a healthier, more balanced world for you and millions of people globally.
Join us in redefining the future of wellbeing!
THE OPPORTUNITY
We are hiring a Senior AI Platform Engineer to our Product Development team in Brazil! This is a Remote – Brazil position, meaning you can work from anywhere within the country. Please note that this role is only open to candidates in Brazil.
Join the ML Development Lifecycle team within our Product Development (PD) organisation, where we are redefining how a global tech company leverages intelligence. We build the foundations that allow hundreds of engineers and data scientists to develop and deploy AI at scale. You will own the evolution of our cloud\-native ecosystem, creating a seamless and high\-performance environment for the next generation of AI\-driven products.
If you are a software\-minded engineer who thrives at the intersection of scalable Infrastructure and ML/AI orchestration, this is your chance to build a world\-class platform that serves millions of users worldwide.
YOUR IMPACT
- Scale the Ecosystem: Evolve and maintain our Kubeflow, Feast and Spark\-on\-Kubernetes infrastructure, ensuring it can handle the increasing complexity of both traditional ML and the new wave of AI.
- Build for Autonomy: Design the internal tools, APIs, and abstractions that empower distributed teams to own their entire lifecycle—transitioning our data culture from "centralised bottleneck" to "self\-service excellence."
- Standardise Engineering Excellence: Collaborate with embedded Data Science teams to adapt software engineering best practices (CI/CD, versioning, testing) to ML\-specific workflows, raising the bar for production\-grade AI across the company.
- Seamless Lifecycle Orchestration: Drive MLOps best practices and define the specific lifecycle requirements for LLMOps, ensuring a frictionless journey from experimental notebooks to robust, production\-grade solutions by providing top\-notch deployment frameworks and automation.
- Cross\-Squad Collaboration: Partner closely with our Infrastructure and Data squads to break silos and integrate ML artifacts into our global Data Catalog, Privacy, and Governance frameworks.
- Live the mission: inspire and empower others by genuinely caring for your own wellbeing and your colleagues. Bring wellbeing to the forefront of work, and create a supportive environment where everyone feels comfortable taking care of themselves, taking time off, and finding work\-life wellness.
WHO YOU ARE
- Platform\-as\-a\-Product Mindset: You treat our AI infrastructure as a product, obsessing over Developer Experience (DX) and continuous feedback loops.
- Collaborative Architect: You excel at balancing distributed team autonomy with global platform standards.
- Pragmatic Innovator: You prioritize robust, scalable production solutions while staying on top of the wave of concepts and practices.
- Systems Thinker: You see the big picture, valuing the integration of ML infrastructure with broader data cataloging and governance.
COMPETENCIES:
- DevOps applied to ML/AI: Deep experience with CI/CD, Infrastructure as Code (Terraform/Crossplane), and Observability.
- Kubeflow, Spark, K8 and AWS: Hands\-on mastery of the Kubeflow platform, Spark engine, AWS ecosystem and Kubernetes.
- Software Foundations: Strong Python skills, focused on building reusable libraries, APIs, and tooling abstractions.
- Solution Architecting \& Adoption: Ability to partner with Data Science teams to translate complex use cases into scalable "paved road" solutions.
WHAT WE OFFER YOU
With thoughtful benefits, emotional wellbeing resources, and a culture that empowers you to take ownership of your role and your wellbeing, we create an environment where you can thrive in all dimensions of your life.
Our flexible benefits program allows you to customize some of the benefits, according to your needs!
Our benefits include:
WELLHUB: Free Gold\+ membership with access to onsite gyms and studios, digital fitness programs, and online wellness resources for meditation, nutrition, mental wellbeing support, and more! Add up to three family members to your plan, ensuring access to wellness for those who matter most to you.
WELLZ: A complete emotional wellbeing program with a unique approach. It offers personalized journeys that combine individual therapy sessions (52 per year) and on\-demand content.
HEALTHCARE: Health, dental, and life insurance.
FLEXIBLE WORK: As a Flexible First company, we offer hybrid and remote options to give you the freedom to work in a way that suits you. The model for this specific role can be discussed with your recruiter and hiring manager. When you join, use our home office reimbursement to set up your home office.
FLEXIBLE SCHEDULE: Flexibility for us isn't just about where we work—it also means being able to shape how and when we get things done. Together with their leaders, employees define schedules that align with their time zones, team needs, and personal routines.
PAID TIME OFF: It's important to take time away from work to recharge.Employees receive vacations after 6 months and additional 3 days off per year \+ 1 day off for each year of tenure (up to 5 additional days) \+ an extra holiday for your birthday!
PAID PARENTAL LEAVE: Welcoming a new child is one of the most special moments in your life. Take the time to be present and enjoy your growing family. We offer 100% paid parental leave to all new parents. Parents giving birth are eligible for an extended leave and a ramp\-back period to return part\-time while they get settled.
CAREER GROWTH: Access world\-class platforms, participate in interactive sessions, build your personalized development roadmap, and explore internal opportunities. We focus on continuous learning and feedback to support your journey toward personal and professional success.
CULTURE: You'll join a team of passionate people who come together to break boundaries, support each other, and create a meaningful impact in workplace wellness. We win together, building trust through open communication and a culture where every perspective matters. Learn more about our shared culture and values here.
And to get a glimpse of life at Wellhub… Follow us on Instagram @lifeatwellhub and LinkedIn!
Diversity, Equity, and Belonging at Wellhub
We aim to create a collaborative, supportive, and inclusive space where everyone knows they belong.
Wellhub is committed to creating a diverse work environment and is proud to be an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, religion, color, sex, gender identity or expression, sexual orientation, age, non\-disqualifying physical or mental disability, national origin, veteran status, or any other basis covered by appropriate law.
Our commitment to inclusion also extends to how we recognize and reward our people. We're proud to be Syndio Fair Pay Certified, reflecting our ongoing dedication to equitable and fair pay practices across our global team. Read more about it here.
Questions on how we treat your personal data? See our Aviso de Privacidade para Candidatos.
\#LI\-REMOTE
\#LI\-BG1
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 Wellhub, 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.
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.
Wellhub AI Hiring
Wellhub has 4 open AI roles right now. They're hiring across AI Software Engineer, AI Product Manager, AI/ML Engineer, Data Scientist. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $170,000 across 1,926 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 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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