Interested in this AI/ML Engineer role at Voya Financial?
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About This Role
*Together we fight for everyone’s opportunity for a better financial future.*
We will do this together — with customers, partners and colleagues. We will fight for others, not against: We will stand up for and champion everyone’s access to opportunities. The status quo is not good enough … we believe every individual and every community deserves access to financial opportunities. We are determined to support both individuals and communities in reaching a better financial future. We know that reaching this future depends on our actions today.
Like our Purpose Statement, Voya believes in being bold and committed to action. We are committed to a work environment where the differences that we are born with — and those we acquire throughout our lives — are understood, valued and intentionally pursued. We believe that our employees own our culture and have a responsibility to foster an environment where we all feel comfortable bringing our whole selves to work. Purposefully bringing our differences together to positively influence our culture, serve our clients and enrich our communities is essential to our vision.
Are you ready to join a company with a strong purpose and a winning culture? Start your Voyage –Apply Now
Get to Know the Opportunity
We’re looking for a hands\-on Applied AI and Engineering leader to help accelerate the practical adoption of AI across Voya Investment Management. This role will focus on applying AI to real business and technology challenges across Investment Management, including investment research, portfolio analytics, operations, distribution, sales, marketing, and technology delivery.
The ideal candidate will be able to identify where AI can create value, partner with business and technology stakeholders, guide engineering teams toward scalable solutions, and help transform successful AI capabilities into reusable assets across the organization.
You will help bridge Investment Management AI engineering, Data Science, and broader Investment Management technology teams while working closely with AI engineering leaders responsible for agentic platforms, runtime architecture, observability, AI security, and engineering standards. The successful candidate should be equally comfortable discussing investment workflows with business partners, AI solution design with engineers, and business outcomes with senior leadership.
Voya is committed to a flexible workplace model. This role may be remote or hybrid, depending on business needs, candidate location, and alignment with the team’s operating model.
The Contributions You'll Make
Scale AI Solutions Across Business Domains
- Ensure AI solutions are designed for scalability, consistency, extensibility, and business adoption across domains.
- Promote successful AI solutions across Investment Management and Distribution and expand them into reusable capabilities for related business use cases.
- Promote collaboration, knowledge sharing, and common practices between AI engineering, data science, and business technology teams.
- Reuse common assets such as agents, prompt libraries, RAG patterns, evaluation approaches, semantic search, APIs, and workflow automation.
Lead Applied AI Across Investment Management
- Build stronger connectivity between domain\-specific AI teams while preserving the business context each team requires.
- Partner with leaders across investment research, portfolio management, operations, distribution, sales, marketing, and technology to identify practical AI use cases.
- Translate ambiguous business challenges into scalable AI\-enabled solutions.
- Help business teams move beyond AI experimentation toward production\-ready capabilities that improve how work gets done.
Promote Engineering Scalability Across IM Technology
- Drive the adoption of AI\-enabled engineering practices across Investment Management technology teams.
- Help technology teams use AI to improve software development, data engineering, testing, documentation, support, and operational productivity.
- Promote reusable engineering patterns, accelerators, templates, and implementation approaches.
- Identify opportunities to apply capabilities built by one team to other areas across Investment Management.
- Reduce duplicated effort by turning successful AI implementations into reusable capabilities, shared services, and common engineering practices.
- Encourage disciplined engineering practices for AI solutions, including versioning, testing, evaluation, monitoring, documentation, and production readiness.
Provide Hands\-On AI Solution Leadership
- Stay actively involved in AI solution design, architecture discussions, prototyping, and implementation planning.
- Guide teams in applying modern AI technologies such as Generative AI, Agentic AI, RAG, semantic search, AI agents, and intelligent automation.
- Evaluate emerging AI tools, frameworks, and platform capabilities for practical use within Investment Management.
- Mentor AI engineers, data scientists, and technology teams on scalable AI implementation practices.
- Help teams balance speed, reliability, cost efficiency, maintainability, and business value.
Partner with AI Engineering and Platform Leaders
- Work closely with AI engineering leaders responsible for agentic platforms, multi\-agent frameworks, runtime architecture, AgentOps, observability, AI security, and engineering standards.
- Ensure AI engineering roadmaps remain aligned with Investment Management business priorities.
- Help define how AI platforms and reusable capabilities can support multiple teams and use cases.
- Promote scalable implementation approaches across platforms such as Microsoft AI Foundry, Microsoft 365 Copilot, Copilot Studio, Databricks, Snowflake Cortex, Azure AI Services, and related AI engineering frameworks.
Build AI Capability and Talent Across IM
- Mentor developing AI engineers, data scientists, and technology professionals.
- Help younger AI team members build stronger understanding of Investment Management business workflows.
- Promote practical AI education, knowledge sharing, and communities of practice across Investment Management technology.
- Foster a culture of experimentation, engineering excellence, business partnership, and measurable outcomes.
- Help build a scalable Applied AI capability that can support growing demand across the organization.
Communicate AI Value and Adoption
- Present AI opportunities, implementation plans, and business outcomes to technology and business leadership.
- Develop clear business cases that connect AI investments to measurable productivity, efficiency, decision quality, and operational improvements.
- Track adoption, value realization, reuse, and scalability of AI solutions.
- Serve as a trusted advisor to business and technology stakeholders on practical AI adoption within Investment Management.
Minimum Knowledge \& Experience
- Bachelor’s degree in Computer Science, Engineering, Data Science, Information Systems, Finance, Business, or a related discipline.
- 10\+ years of experience in technology, data science, AI, analytics, software engineering, or digital transformation.
- 5\+ years of leadership experience managing technical teams, cross\-functional initiatives, or enterprise\-scale technology programs.
- Experience delivering AI, analytics, data science, automation, or software solutions into production environments.
- Strong understanding of Generative AI, Agentic AI, AI engineering, intelligent automation, and modern software delivery practices.
- Practical experience translating business problems into AI\-enabled solution designs.
- Ability to guide engineers and data scientists while also engaging effectively with business stakeholders.
- Strong communication skills with the ability to explain AI concepts, business value, and implementation tradeoffs to technical and non\-technical audiences.
- Demonstrated ability to drive adoption, influence across teams, and deliver measurable business outcomes.
Preferred Knowledge \& Experience
- Experience in Asset Management, Investment Management, Wealth Management, Financial Services, or another regulated business environment.
- Experience supporting investment research, portfolio management, investment operations, distribution, sales, marketing, or client\-facing business functions.
- Hands\-on familiarity with modern AI platforms and engineering tools such as:
+ Databricks Genie AI
+ Snowflake Cortex
+ Microsoft AI Foundry
+ Microsoft 365 Power Platform
+ Python \& SQL
+ GitHub Copilot or similar AI\-assisted engineering tools
- Experience with:
+ Agentic AI
+ Multi\-agent architectures
+ Retrieval\-Augmented Generation
+ Semantic search
+ Prompt engineering
+ AI evaluation and monitoring
+ AI workflow automation
+ API and tool integration
+ Reusable engineering frameworks
- Experience leading or mentoring AI engineers, data scientists, software engineers, or technology teams.
- Experience creating reusable technical capabilities, shared services, engineering accelerators, or common implementation patterns.
\#Remote
\#LI\-LW1
Compensation Pay Disclosure:
Voya is committed to pay that’s fair and equitable, which means comparable pay for comparable roles and responsibilities.
The below annual base salary range reflects the expected hiring range(s) for this position in the location(s) listed. In addition to base salary, Voya offers incentive opportunities (i.e., annual cash incentives, sales incentives, and/or long\-term incentives) based on the role to reward the achievement of annual performance objectives. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Voya Financial is willing to pay at the time of this posting.
Actual compensation offered may vary from the posted salary range based upon the candidate’s geographic location, work experience, education, licensure requirements and/or skill level and will be finalized at the time of offer. Salaries for part\-time roles will be prorated based upon the agreed upon number of hours to be regularly worked.
$171,300 \- $214,120 USDBe Well. Stay Well.
Voya provides the resources that can make a difference in your lives. To us, this means thriving physically, financially, socially and emotionally. Voya benefits are designed to help you do just that. That’s why we offer an array of plans, programs, tools and resources with one goal in mind: To help you and your family be well and stay well.
What We Offer
- Health, dental, vision and life insurance plans
- 401(k) Savings plan – with generous company matching contributions (up to 6%)
- Voya Retirement Plan – employer paid cash balance retirement plan (4%)
- Tuition reimbursement up to $5,250/year
- Paid time off – including 20 days paid time off, nine paid company holidays and a flexible Diversity Celebration Day.
- Paid volunteer time — 40 hours per calendar year
Critical Skills
At Voya, we have identified the following critical skills which are key to success in our culture:
- Customer Focused: Passionate drive to delight our customers and offer unique solutions that deliver on their expectations.
- Critical Thinking: Thoughtful process of analyzing data and problem solving data to reach a well\-reasoned solution.
- Team Mentality: Partnering effectively to drive our culture and execute on our common goals.
- Business Acumen: Appreciation and understanding of the financial services industry in order to make sound business decisions.
- Learning Agility: Openness to new ways of thinking and acquiring new skills to retain a competitive advantage.
Equal Employment Opportunity
*Voya Financial is an equal\-opportunity employer. Voya Financial provides equal opportunity to qualified individuals regardless of race, color, sex, national origin, citizenship status, religion, age, disability, veteran status, creed, marital status, sexual orientation, gender identity, genetic information, or any other status protected by state or local law.*
Reasonable Accommodations
*Voya is committed to the inclusion of all qualified individuals. As part of this commitment, Voya will ensure that persons with disabilities are provided reasonable accommodations. If reasonable accommodation is needed to participate in the job application or interview process, to perform essential job functions, and/or to receive other benefits and privileges of employment,* *please* *reference* *resources for applicants with disabilities**.*
Salary Context
This $171K-$214K 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 Voya Financial, 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. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($192K) sits 10% below the category median. Disclosed range: $171K to $214K.
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.
Voya Financial AI Hiring
Voya Financial has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Windsor, CT, US, New York, NY, US. Compensation range: $198K - $214K.
Location Context
AI roles in New York pay a median of $220,000 across 1,650 tracked positions.
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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