Software Dev Engineer II, SIFT
Here's the kind of thing you'd own and solve:
- Scale what's already working.
Take services that are live and heavily used and make them faster, sturdier, and ready for the next order of magnitude of users and data.
- Grow what the product can connect to.
Expand the set of tools and integrations the system exposes through the Model Context Protocol (MCP), so the product can plug into more data sources and let AI assistants do more on a user's behalf - safely and reliably.
- Raise the quality bar on AI output.
Improve how the product turns messy, free-form input into trustworthy, structured, searchable records - with smarter summarization, tagging, and quality gating so strong output ships and weak output doesn't.
- Extend the product thoughtfully.
Add new capabilities and data sources to a system people already depend on, without disrupting the workflows they rely on every day.
- Make the trends sharper.
Improve how the product spots patterns across a huge body of individual stories, so people see what's emerging without digging for it.
- Let people talk to their data.
Build and refine the natural-language experiences that let someone ask for what they need - and get an answer built from real data, and only the data they're allowed to see.
- Keep it healthy.
Own the reliability and operations of what you build, dig into production issues when they happen, and raise the operational bar for the whole team.
What sets this apart: you're joining something that already succeeded - and the interesting work is everything that comes next. You get the best of both worlds: the real users, real feedback, and real impact of a product in heavy use, plus a wide-open set of hard problems around scaling it, deepening the AI, and taking it places it hasn't gone yet. You'll make architectural calls that matter, see your impact quickly because the people using it will tell you exactly what's working, and be expected to lift the engineers around you - mentoring, reviewing designs, and being someone who makes the whole team sharper, not just your own code.
We're looking for someone who's built and operated real backend systems at scale, is comfortable improving a live product without breaking it, and is genuinely excited about applying AI to a hard, valuable problem rather than chasing the buzz. Hands-on experience with generative AI and large language models is a strong prerequisite - and familiarity with Amazon Bedrock in particular will set you up to make an impact fast. If you like owning things end to end, caring about the person on the other side of the screen, and raising the bar on something people already love - you'll feel at home here.
Key job responsibilities
You'll own features end to end - from the user interface down to the backend services behind them - designing, building, and running them in production, where real people rely on them every day.
- Own whole features from the screen a user sees to the services and data underneath, making the design calls across the full stack and shipping each one to production.
- Build and scale the services that turn raw, free-form field input into structured, searchable, trustworthy records - keeping them fast and reliable as usage grows.
- Expand the tools and integrations the product exposes through the Model Context Protocol (MCP), connecting it to more data sources and letting AI assistants safely do more on a user's behalf.
- Wire large language models into the product and raise the quality bar on what they produce - sharper summarization, tagging, and quality gating so strong output ships and weak output doesn't.
- Build the natural-language experiences that let people ask for what they need and get answers grounded in real data - and only the data they're allowed to see.
- Write the design docs, run code reviews, debug the gnarly production issues, and mentor the engineers around you.
A day in the life
You're working where solid engineering meets applied AI - shipping real features people use the moment they go out.
You might push a feature you own from the screen a user sees down to the service behind it: write the code, get it reviewed, shepherd it through testing into production. A good chunk of your time goes into making the AI better - tuning how it drafts and summarizes, then checking the output against real examples to prove it improved. You'll join a short daily standup and review teammates' code.
Some days stretch further - debugging production issues, or prototyping an idea.
About the team
We build AI that changes how the sales field works. Our users are the people talking to AWS customers and partners every day - and our job is to turn the stories they gather into something they can use right away. We capture their input, surface the patterns across it, and draft the reports that used to eat hours of someone's week. You'll work shoulder to shoulder with engineers, product, and the field teams who depend on what we ship, to help shape where the product goes next. We move fast, listen to our users, and care about getting it right.
BASIC QUALIFICATIONS
- 3+ years of non-internship professional software development experience
- 2+ years of non-internship design or architecture (design patterns, reliability and scaling) of new and existing systems experience
- 1+ years of designing and developing large-scale, multi-tiered, multi-threaded, embedded or distributed software applications, tools, systems, and services using: C#, C++, Java, or Perl experience
- 1+ years of Object Oriented Design experience
- Bachelor's degree or foreign equivalent in Computer Science, Engineering, Mathematics, or a related field
- Knowledge of Python and/or C++ programming
PREFERRED QUALIFICATIONS
- 3+ years of full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations experience
- Bachelor's degree in computer science or equivalent
- Knowledge of Machine Learning and LLM fundamentals, including transformer architecture, training/inference lifecycles, and optimization techniques
The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits.
USA, MA, Boston - 143,700.00 - 194,400.00 USD annually
