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About This Role
Forward Financing is a financial technology company based in Boston, Massachusetts with team members throughout the United States, Dominican Republic, and Canada. The company is on a mission to unlock the capital that fuels small businesses across America. Recognized as a Best Place to Work by Built In Boston and certified as a Great Place To Work, Forward is investing in its employees, technology, and customer experience – with long\-term success in mind every step of the way.
Forward Financing is a financial technology company headquartered in Boston, MA with an operational hub in the Dominican Republic, a satellite office in Salt Lake City, Utah and a growing team in Canada. Forward is on a mission to unlock the capital that fuels small businesses across America. Recognized as a Best Place to Work by Built In Boston and certified as a Great Place To Work, Forward is investing in its employees, technology, and customer experience – with long\-term success in mind every step of the way.
Our Engineering team serves as a key driver of innovation at Forward Financing. We build the software that powers a FinTech product that serves Small Businesses across the country. A key part of our strategy involves leveraging data from dozens of sources to power our AI/ML initiatives and our analytical capabilities. We're looking for an experienced Senior Software Engineer to join our team, responsible for the systems that power our online (real\-time) feature store — the infrastructure that serves ML features to production models with low latency and high reliability.
In this role you will:
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- Design, build, and operate the online feature store — the real\-time serving layer that delivers ML features to production models with low latency and strong consistency guarantees
- Build and maintain data pipelines (batch and streaming) that compute, validate, and publish features from source systems into the online and offline stores
- Work on the data models and Postgres schemas that back real\-time feature serving, optimizing for query performance, freshness, and scale
- Act as a technical leader for feature store infrastructure; help drive enhancements to quality, scalability, reliability, and observability for the systems ML models depend on
- Partner closely with our Data Science, Analytics Engineering, Product Management, and Application Development teams to translate business requirements into production\-grade engineering solutions
- Contribute to mentoring for junior engineers
- Contributing to best practices and raising the bar through thoughtful code reviews and contributions to technical design discussions
Why you should apply:
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- Mission\-driven company: Forward is a trusted source of fast, flexible funding for small businesses that have often been underserved by traditional financing options. When you join the team, you will help ensure all small businesses have access to the financial support they need to succeed.
- Flexibility is a top priority: Our employees are empowered to choose where they want to work (whether that's from home, in the office, or a combination of both).
Role Requirements:
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- 5\+ years of software experience, with a focus on backend systems (Python required) and 2\-3 years of MLOps experience
- Strong experience with relational databases (Postgres preferred) — schema design, query optimization, and operating databases under production load
- Experience building and operating real time inference systems
- Understanding of the unique reliability and correctness demands of ML\-serving infrastructure (data freshness, training/serving skew, feature consistency)
- Experience in mutli\-service architectures and design patterns
- Experience in Agile software development
- Typically has a Bachelor's degree in Computer Science, Data Engineering, or a related field, or additional relevant experience
Bonus Qualifications:
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- Experience with feature stores (e.g., Feast, Tecton, SageMaker Feature Store) or building an equivalent system in\-house
- Experience with MLOps tooling (e.g., MLflow, Airflow, dbt) and ML model deployment/serving patterns
- Experience designing and implementing complex systems across multiple software applications and/or languages
- Excellent written and verbal communication
- Ability to influence others
- Demonstrated project management skills
Compensation:
Annual Salary: $175,000 \- $220,000 CAD
Annual Bonus: You have the potential to earn an additional 10% annual bonus.
At Forward Financing, we're committed to fair and transparent compensation. We believe in providing a compensation package that recognizes your skills, experience, and the unique value you bring to our team. We take a market\-basedapproach to pay, regularly reviewing benchmark data to ensure our compensation remains competitive, equitable, and aligned with our performance\-driven culture.
Final offers are determined by a variety of factors, including the candidate's qualifications, relevant experience, specific skills, and internal equity. This approach ensures that our compensation is competitive and equitable. Your recruiter will provide specific details on the expected base and variable earnings as it pertains to this specific role. This position is to fill an existing vacancy.
Our Core Values:
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- Drive the Mission: We believe in financial opportunity for underserved small businesses. We say “yes” when others say “no.”
- Keep It Real: We value direct communication, candid feedback, and authenticity. We are an open book.
- Act With Kindness: We create an environment where caring is cool and helping is the norm. We do the right thing.
- Shoot for Extraordinary: We are inspired by innovative thinking and continuous improvement. We never settle for yesterday’s best.
About Us:
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Forward Financing is a financial technology company based in Boston, Massachusetts with an operational hub in Santiago, Dominican Republic, on a mission to unlock the capital that fuels small businesses across America. Whether facing challenges accessing traditional financing or simply needing a convenient, flexible solution, Forward is committed to funding more of the millions of small businesses nationwide. Forward offers revenue\-based financing – delivering an upfront sum of working capital in exchange for a set amount of the business’s future revenue. By simplifying the requirements, streamlining the process, and using advanced proprietary technology, Forward is often able to deliver approvals within hours and funds that same day – giving more businesses the financial opportunity they need to thrive. Plus, with their dedicated teams and award\-winning service, customers get personalized support when they need it most.
Since 2012, Forward has expanded access to capital by providing over $3\.5 billion in funding to more than 71,000 small businesses. The company is A\+ rated by the Better Business Bureau with an Excellent / 4\.7 stars rating on Trustpilot.com. Recognized as a Best Place to Work by Built In Boston and certified as a Great Place To Work, Forward is dedicated to empowering both its team and the customers they serve, helping them succeed and thrive.
Total Rewards:
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Additionally, we offer a comprehensive total rewards package, including but not limited to: medical, dental, vision, a flexible time\-off policy, paid parental leave, RRSP match, wellness reimbursement, volunteering days, annual professional development budget, and charitable donation match.
Forward is proud to be a remote\-first company, keeping workplace flexibility a top priority for our employees. As a business, we are focused on impact; we are more concerned with your contributions to the success of the company than where you get your work done. To help facilitate in\-person collaboration, employees are welcome to work from one of our premiere office locations.
When we aren’t collaborating to drive business and support our customers, we’re finding virtual and in\-person ways to get to know our colleagues, celebrate team wins, and have fun together!
Equal Opportunity Employment Information
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It is the policy of Forward Financing to ensure equal employment opportunity without discrimination or harassment on the basis of race, religion, national origin, status, age, sex, sexual orientation, gender identity or expression, marital or domestic/civil partnership status, disability, veteran status, genetic information, or any other basis protected by law. We welcome and encourage applications from people with disabilities. Please let us know should you require any accommodations. As part of our commitment to a streamlined and equitable hiring experience, we use AI tools to assist with candidate screening and assessment.
Compensation Range: CA$175K \- CA$220K
Salary Context
This $175K-$220K range is above the 75th percentile for MLOps Engineer roles in our dataset (median: $177K across 20 roles with salary data).
View full MLOps Engineer salary data →Role Details
About This Role
MLOps Engineers build the infrastructure that keeps ML models running in production. They own CI/CD pipelines for model deployment, monitoring for data drift and model degradation, and the tooling that lets data scientists ship faster. If ML Engineers build the models, MLOps Engineers build the roads those models travel on.
The job is fundamentally about reliability and velocity. Data scientists want to iterate fast. Product teams want stable predictions. Your job is to make both happen simultaneously. That means building deployment pipelines that catch regressions before they hit production, monitoring systems that alert on data drift before it degrades model performance, and self-service tooling that lets data scientists deploy without filing a ticket.
Across the 3,708 AI roles we're tracking, MLOps Engineer positions make up 1% of the market. At Forward Financing, this role fits into their broader AI and engineering organization.
MLOps demand tracks closely with production ML adoption. As more companies move models from notebooks to production, the need for MLOps grows. The role is well-established at large tech companies and growing fast at mid-stage startups that are hitting the 'our models work in notebooks but break in production' phase.
What the Work Looks Like
A typical week involves: debugging a model deployment that's serving stale predictions, building a new monitoring dashboard for a feature team, writing Terraform for GPU-enabled inference clusters, reviewing pull requests for the ML platform's CI/CD pipeline, and meeting with data scientists to understand their pain points. You're the bridge between ML and infrastructure.
MLOps demand tracks closely with production ML adoption. As more companies move models from notebooks to production, the need for MLOps grows. The role is well-established at large tech companies and growing fast at mid-stage startups that are hitting the 'our models work in notebooks but break in production' phase.
Skills Required
Kubernetes, Docker, and cloud infrastructure are baseline. Most roles want experience with ML-specific tooling: MLflow, Kubeflow, Weights & Biases, or similar. Strong DevOps fundamentals matter more than ML theory. You need to understand model serving (TorchServe, Triton, vLLM), monitoring (Prometheus, Grafana), and infrastructure-as-code (Terraform, Pulumi).
GPU infrastructure knowledge is increasingly valuable as LLM inference becomes a major cost center. Understanding GPU scheduling, multi-node training setups, and inference optimization (quantization, batching, caching) puts you in the top tier. Experience with model registries and feature stores rounds out the profile.
Good MLOps postings specify their ML stack, infrastructure scale, and the problems they're solving (deployment velocity, cost optimization, monitoring gaps). Red flag: companies that want MLOps but don't have any models in production yet. You'll end up doing general DevOps instead.
Compensation Benchmarks
MLOps Engineer roles pay a median of $220,000 based on 47 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($197K) sits 10% below the category median. Disclosed range: $175K to $220K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Forward Financing AI Hiring
Forward Financing has 1 open AI role right now. They're hiring across MLOps Engineer. Based in Ontario, CA, US. Compensation range: $220K - $220K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 median).
Career Path
Common paths into MLOps Engineer roles include DevOps Engineer, Platform Engineer, Data Engineer.
From here, career progression typically leads toward ML Platform Lead, Infrastructure Architect, Engineering Manager.
DevOps engineers with ML curiosity have the shortest path. You already understand deployment, monitoring, and infrastructure. Add ML-specific knowledge (model serving, data pipelines, experiment tracking) and you're competitive. The career ceiling is high: ML Platform Lead roles at top companies pay well because the infrastructure complexity is enormous.
What to Expect in Interviews
Interviews emphasize infrastructure and reliability. Expect questions about CI/CD for ML models, monitoring for data drift, and how you'd design a model serving platform that handles 10K requests per second. Coding rounds focus on Python and infrastructure-as-code (Terraform, Helm). Be ready to discuss tradeoffs between different model serving frameworks and how you'd handle rollback when a new model degrades performance.
When evaluating opportunities: Good MLOps postings specify their ML stack, infrastructure scale, and the problems they're solving (deployment velocity, cost optimization, monitoring gaps). Red flag: companies that want MLOps but don't have any models in production yet. You'll end up doing general DevOps instead.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
MLOps demand tracks closely with production ML adoption. As more companies move models from notebooks to production, the need for MLOps grows. The role is well-established at large tech companies and growing fast at mid-stage startups that are hitting the 'our models work in notebooks but break in production' phase.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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