Interested in this AI/ML Engineer role at Achieve?
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Company Description
Achieve is a leading digital personal finance company. We help everyday people move from struggling to thriving by providing innovative, personalized financial solutions. By leveraging proprietary dataand analytics, our solutions are tailored for each step of our member's financial journey to include debt consolidation, personal loans, home equity loans, financial tools and education. Every day, we get to help our members move their finances forward with care, compassion, and empathetic touch. We put people first and treat them like humans, not account numbers.
The fintech industry is large and growing. Over $11T in home equity is sitting untapped across US homeowners - most haven’t borrowed against it yet, while personal loan balances just hit a record $276Bn from four years in a row of growth, with no signs of slowing. Consumers continue to face mounting debt. Credit card delinquencies are up 38%, leading to more demand for debt relief services. Achieve continues to support members and meet them wherever they are on their journey. Since 2002, Achieve has grown into one of the largest private consumer fintech unicorns in the U.S., with over $57B in enrollments for our industry-leading, tech-enabled debt resolution services business, and over $16Bn in personal and home loans originations.
Job Description About this role:
Achieve is redefining how customers experience financial help and its internal operating model, with AI at the center rather than bolted on. We're looking for a Director of Product Management to own the two foundational enterprise capabilities that make that possible:
- Data Platform - the governed data foundation and semantic layer that enable agentic workflows, natural language analytics and standardized reporting, along with the enterprise consumer data products built on it
- Agentic AI Platform - the shared harness and infrastructure that lets teams across Achieve build, launch & govern their own workflows and customer-facing agents
This is initially a hands-on senior individual contributor role: you will define the platform strategy and roadmap, inform the early technology and vendor choices and work alongside engineering, data, security, risk, compliance teams to get both platforms launched and operating at scale. As the platforms mature and become more embedded into the business, we expect this role grows into a team leadership position.
Why this role matters:
A shared platform foundation enables AI at Achieve to operate at scale, so that every new agent, voice experience, and analytics capability benefits from what came before. Agents are faster to build, cheaper to operate, safer to run and better informed because they share data, context, learnings and governance. The choices made by this leader early on will shape how Achieve builds and operates for years. Done well, this role will:
- Replace static dashboards and traditional BI reports with a real-time end-to-end view of the business, where people can ask questions in plain language and get trusted answers
- Deliver the governed data foundation and shared agent harness that let teams design, launch, measure, and govern their own AI-driven workflows
- Help teams rethink how they operate around agents, with guardrails that scale with risk, in a regulated industry where trust and auditability are non-negotiable
What you'll do
Data Platform
- Define and execute strategy for the enterprise data platform, including target architecture, sequencing, and success metrics
- Lead migration of existing data, ETL processes, and dashboards with minimal business disruption
- Optimize replication, storage, and compute to materially reduce inefficiencies and cost
- Own the roadmap for the enterprise data products and services across identity, consent, product interaction and insights that are leveraged by multiple product teams
- Enable agentic and natural language analytics so business users can self-serve trusted insights
Context Engineering & Semantics
- Own the enterprise semantic layer and business glossary: governed definitions that serve both BI users and agents
- Define how agents get the right information, tools, instructions, and memory for each task, and how that context is assembled, versioned, tested, and kept current
- Set standards for measuring context quality, including retrieval accuracy, grounding, freshness, and cost
- Define access models so agents see only what each user and task allows
Agentic AI Platform
- Set the vision and multi-year roadmap for the enterprise agent harness: the shared framework for building, orchestrating, evaluating, and monitoring agents
- Build a small number of reference workflows that prove the harness and give other teams a model to follow
- Enable teams across operations, servicing, sales, and corporate functions to build and run their own workflows on the platform, prioritized by value and feasibility
- Enable new capabilities for customer-facing AI experiences and voice agents, including authentication, latency, human handoff, and compliance
- Support AI-driven SDLC to measurably improve engineering throughput and quality
- Track new AI frontier models, agent frameworks, and emerging standards, and translate what matters into platform decisions, including when to adopt, swap, or avoid
Governance & Change
- Build safety into the platform design: evaluation, audit trails, approval thresholds, human-in-the-loop checkpoints, and rollback, matched to risk, including judgment on where AI should not be used
- Guide teams to rethink how they operate, not just automate existing operational steps and report progress and tradeoffs on the AI transformation to senior executives
- Make infrastructure, token, and inference cost visible and manageable for the teams that incur it, and manage vendor and model spend as part of the roadmap
- Partner with Data Engineering, InfoSec, and Compliance on building governance, lineage, quality, privacy, and access controls into the platform design
Qualifications What you bring
- 10+ years of product management experience owning platform, data, infrastructure, including services and APIs consumed by other teams
- A platform mindset: with examples of building shared infrastructure that delivered operating leverage to the company, rather than a series of standalone point solutions
- Experience leading large data or platform migrations, and managing cost across cloud, storage, model, and vendor spend
- Knowledge of modern data architecture, including warehouses and lakehouses (such as Snowflake and Databricks), ETL/ELT, event streaming, semantic layers, metric stores, and ontologies
- Experience building data governance into a platform: lineage, quality, privacy, and access controls, ideally in a regulated business such as financial services
- Working understanding of security, fraud and regulatory risks specific to AI systems and sensitive financial data, and experience building controls for regulated businesses
- Comfort working with code, prompts, and evaluation frameworks, with the depth to earn credibility with engineering teams.
- Ability to link platform work to the business outcomes it enables and show value that is indirect rather than directly attributed
- Proven ability to influence senior stakeholders and drive outcomes without direct authority, and to explain technical concepts to executive audiences
Strong plus
- Recent, demonstrated experience building agentic workflows for scaled operational use, and the ability to show what you built, what broke, and what you changed
- Experience managing model, token, and inference cost for AI workloads
- Experience with customer-facing or voice AI, including authentication, latency, human handoff, and compliance
- Experience applying AI to the software development lifecycle
- Experience mentoring or leading product managers
Achieve Leader Model Expectations (Director Level)
In accordance with Achieve’s Lead Forward Rubric, this Director position requires demonstrated excellence across four foundational leadership pillars:
1. Trusted, Steady Leadership
Maintains composure and steady communication during high-pressure pivots. Instills trust across teams through consistency, fairness, and transparent communication. Bridges the competing stakeholder priorities, using credibility and relationships to align divergent teams around shared goals.
2. Focused & Agile
Sets high-impact objectives that streamline cross-team execution and remove low-value work. Actively leads teams through change by providing context, engaging managers, addressing resistance, and adjusting plans as needed. Navigates complex ambiguity with confidence, evaluating options and providing sufficient clarity for teams to act while remaining flexible.
3. Own the Outcome
Drives consistently high performance through disciplined execution and clear, transparent accountability practices. Standardizes core cross-team workflows so execution remains dependable without relying on crisis management or individual heroics. Actively identifies where workflows break down between managers, replacing manual workarounds with repeatable, scalable procedures.
4. Grow People & Capability
Identifies key talent, holds managers accountable for developing their teams, and supports active coaching. Drives strong leadership development across teams and builds capable managers. Invests in coaching, feedback, and growth opportunities to elevate capability and consistency across the function.
Additional Information Achieve well-being with:
- Hybrid and remote work opportunities
- 401 (k) with employer match
- Medical, dental, and vision with HSA and FSA options
- Competitive vacation and sick time off, as well as dedicated volunteer days
- Access to wellness support through Employee Assistance Program, Talkspace, and fitness discounts
- Up to $5,250 paid back to you on eligible education expenses
- Pet care discounts for your furry family members
- Financial support in times of hardship with our Achieve Care Fund
- A safe place to connect and a commitment to diversity and inclusion through our six employee resource groups
Note: We will be unable to facilitate H1-B Visa transfer or sponsorship, along with STEM-OPT Visa.
Work from home/hybrid:
We are proudly offering hybrid options in the Phoenix, AZ and San Francisco, CA metro market. We are offering 100% remote work in other approved PST, MST, CST locations.
Salary Range: $220,000 to $250,000 salary + bonus + benefits.
This information represents the expected salary range for this role. Should we decide to make an offer for employment, we'll consider your location, experience, and other job-related factors.
Join Achieve, change the future.
At Achieve, we’re changing millions of lives.
From the single parent trying to catch up on bills to the entrepreneur needing a loan for the next phase of growth, you’ll get to be a part of their journey to a better financial future. We’re proud to have over 3,000 employees in mostly hybrid and 100% remote roles across the United States with hubs in Arizona, California, and Texas. We are strategically growing our teams with more remote, work-from-home opportunities every day to better serve our members. A career at Achieve is more than a job—it’s a place where you can make a true impact, have a sense of belonging, establish a fulfilling career, and put your well-being first.
Attention Agencies & Search Firms: We do not accept unsolicited candidate resumes or profiles. Please do not reach out to anyone within Achieve to market your services or candidates. All inquiries should be directed to Talent Acquisition only. We reserve the right to hire any candidates sent unsolicited and will not pay any fees without a contract signed by Achieve’s Talent Acquisition leader.
Salary Context
This $220K-$250K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Achieve, 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 in Demand for This Role
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 $211,950 based on 13,048 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,300. This role's midpoint ($235K) sits 11% above the category median. Disclosed range: $220K to $250K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Achieve AI Hiring
Achieve has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in San Mateo, CA, US. Compensation range: $250K - $250K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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