Interested in this AI/ML Engineer role at Shift Technology?
Apply Now →About This Role
Shift delivers AI agents that transform insurers' most critical work. By combining deep industry expertise and unmatched data resources, Shift provides proven results that have earned the trust of hundreds of the world's leading insurers. Our insurance-grade AI is accurate, explainable, and secure—empowering human experts to move with unmatched speed, total confidence, and a renewed focus on the people they serve.
Our culture is built on innovation, trust, and a drive to transform the insurance industry through our SaaS platform. We come from more than 50 different countries and cultures and together we are creating the future of insurance.
Learn more at www.shift-technology.com
As a Project Manager, you will join our Delivery team to spearhead the deployment of Shift's AI solutions for US healthcare clients. You will act as the cornerstone between our technical teams and client stakeholders, ensuring projects move from initial scoping to scalable, long-term success. Your goal is to anticipate risks and build strong, trust-based relationships that allow our products to deliver measurable value for our clients. This hybrid role is essential for driving the collaborative execution that defines how we win together at Shift, and should expect to be in the office 2 days per week.
What Your Impact Looks Like:
At Shift, we value ownership from day one. In this role, you will:
- Jump In & Get Things Done: Take proactive ownership of project milestones and resolve blockers quickly to keep delivery on track. Coordinate with our technical program management team to ensure alignment between product roadmap and project plan as well as the execution of the release process across the implementation lifecycle.
- Simplify the Complex: Translate technical and healthcare business requirements and project plans into clear, accessible language for internal teams, customers, and C-level stakeholders.
- Decide with Data & EQ: Manage complex client relationships by balancing hard project data with emotional intelligence to navigate high-pressure situations.
- Test, Learn, Scale: Experiment with new project management methods and share these learnings to help the delivery organization improve speed and quality.
- Fix Issues Without Drama: Address delivery challenges directly and objectively, focusing on collaborative solutions rather than placing blame.
- Communicate with Impact: Adapt your communication style to align diverse stakeholders, from operational teams to senior management, around shared project goals.
- Cross the Finish Line: Ensure projects are delivered fully and meet agreed-upon standards, validating that outcomes truly meet client expectations before closure and transition successfully into ongoing operations.
What You'll Need to Succeed:
We are looking for a Shifter who is...
- Impact Oriented: You bring at least 5 years of technical project management experience with a consulting or SaaS company, including experience working in the US healthcare industry with health plan clients. You are comfortable working with healthcare data and understand the workflows, systems, and operational complexities of health plan environments. You have a proven track record of managing complex projects and understand how to turn project milestones into measurable business impact.
- Methodology-Minded: You hold a PMP or comparable nationally recognized project management certification and use a structured approach to build scalable processes and implementation plans, but understand how to apply these frameworks flexibly.
- Healthcare Fluent: You understand the US healthcare payer environment and can effectively partner with health plan stakeholders across business, operations, technology, and data functions. Familiarity with healthcare claims data, payer workflows, and implementation of healthcare technology solutions is strongly preferred.
- Growth Minded: You are always looking for a better way to do something. You are eager to test, learn, and adapt your project management methods by integrating AI-driven tools into your own workflow, using technology to automate the routine and scale your impact as Shift grows.
- Collaborative: You thrive in cross-functional environments, partnering naturally with Product, Engineering, Operations, and GTM teams to deliver unified outcomes.
- Client Focused: You are committed to exceptional service but prioritize the project's long-term success over short-term requests. You have the professional courage to stand your ground under pressure and navigate difficult client conversations with data and integrity.
Requirements:
- 5+ years of project management experience in healthcare technology, SaaS, consulting, or a related environment.
- Demonstrated experience working with US health plans and health plan stakeholders.
- Experience managing healthcare technology implementations involving complex data, integrations, workflows, and multiple internal and client stakeholders.
- Working knowledge of healthcare claims data and payer operational processes.
- PMP or comparable nationally recognized project management certification preferred.
- Bachelor's degree or equivalent relevant professional experience.
To support our permanent, full time employees at every stage of their careers and lives, we provide a competitive total rewards and benefits package. Here are the global benefits we'd like to highlight:
- Flexible remote and hybrid working options
- Competitive Salary and a variable component tied to personal and company performance
- Multiple Learning and Development opportunities, including Focus Fridays, a half-day each month to focus on learning and personal growth
- Generous PTO and paid holidays
- Mental health benefits
- 2 MAD Days per year (Make A Difference Days for paid volunteering)
Additional benefits may be offered by country, based on your eligibility - ask your recruiter for more information. Intern and Apprentice positions may receive some of these benefits - ask your recruiter for more details.
AI tools are used to help review applications for this role. Read our AI in Recruitment Notice**for what the AI considers, how to request a human review, and our most recent bias audit.
At Shift we strive to be a diverse and inclusive workforce. We welcome applications from and hire people who will contribute to the diversity of our company, without regard to race, color, religion, marital status, age, national or ethnic origin, physical or mental disability, medical condition, pregnancy, genetic information, gender identity or expression, sexual orientation, or other non-merit criteria. Shift Technology is committed to providing reasonable accommodations for qualified individuals with disabilities in our application and employment process. Should you require accommodation, please email [email protected] and we will work with you to meet your accessibility needs.
Please be aware of scammers and only trust correspondence that comes from emails ending in "shift-technology.com". We will never do initial outreach to you via Whatsapp/Text/SMS, never ask for banking information or personal identification numbers (ex. Social Security Number) as part of our recruitment process.
Shift Technology does not accept unsolicited CVs from recruiters or employment agencies in response to the Shift Technology Careers page or a Shift Technology social media post. Any unsolicited CVs, including those submitted directly to hiring managers, are deemed to be the property of Shift Technology.
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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Shift Technology, 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 $212,000 based on 11,857 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Shift Technology AI Hiring
Shift Technology has 2 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Positions span Boston, MA, US, São Paulo, São Paulo, Brazil.
Location Context
AI roles in Boston pay a median of $215,000 across 323 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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