Accountabilities:

Define enterprise scale-readiness standards by establishing measurable targets for latency, throughput, error rates, and capacity across critical workloads.

Develop and maintain a shared scalability scorecard, aligning engineering teams around service-level objectives, error budgets, and performance expectations.

Identify and eliminate major system bottlenecks using production data, profiling tools, reproducible benchmarks, and representative workload testing.

Partner with service owners to implement performance improvements and validate their impact under realistic production conditions.

Lead architectural initiatives involving sharding, partitioning, caching, back-pressure, workload isolation, and multi-region readiness.

Design safe and reliable approaches for high-volume data migrations and evolving enterprise infrastructure requirements.

Build reusable engineering tools, including lightweight benchmarks, profiling frameworks, and test environments that enable teams to assess performance independently.

Introduce scalability reviews and proportionate release-readiness checks that identify risks early and support confident delivery.

Lead technical investigations following major incidents, identify systemic causes, and coordinate lasting improvements to reduce recurring reliability issues.

Apply AI-assisted tools and automation to engineering workflows, including profiling, workload modeling, synthetic traffic generation, anomaly detection, and guided incident response.

Establish appropriate guardrails for AI-enabled engineering practices, evaluate their effectiveness, and measure their operational impact.

Communicate technical priorities, architectural trade-offs, and delivery outcomes to engineering teams and executive stakeholders, connecting platform investments to customer experience and business objectives.

Influence engineering practices across organizational boundaries, building alignment around shared outcomes and clear ownership.

Requirements

12+ years of relevant software engineering experience, including a demonstrated track record of scaling complex, multi-tenant SaaS platforms and delivering measurable production improvements.

Deep expertise in performance engineering and distributed systems, including capacity planning, sharding, partitioning, caching, back-pressure, tenant isolation, and multi-region architectures.

Experience managing large-scale workloads and migrations while maintaining system reliability, availability, and performance.

Strong hands-on engineering skills, with the ability to investigate unfamiliar systems, develop and test hypotheses, write production code, and coordinate safe implementation with service owners.

Excellent technical judgment and the ability to evaluate architectural trade-offs using evidence, balancing immediate performance constraints with broader platform investments.

Proven ability to lead through influence, align multiple engineering teams, clarify ownership, and establish practical standards that support consistent execution.

Strong communication skills, including the ability to explain complex technical decisions and their business implications to both engineering teams and executive audiences.

Practical experience applying AI tools or automation to engineering workflows, with the ability to assess their value, limitations, privacy and security considerations, and operational risks.

A disciplined, curious approach to emerging technologies, with an emphasis on validating results and measuring real-world impact.

Ability to work remotely from the U.S. East Coast. Candidates must be able to travel to the Boston headquarters at least once per quarter.

Willingness to travel up to 10% for onboarding, team meetings, client or partner engagements where applicable, and industry events. Travel is coordinated in advance.

This is a senior individual contributor position with no direct reports, requiring technical leadership, hands-on delivery, and organization-wide influence.

Benefits

Annual base salary: $244,000–$366,000 USD across U.S. locations. The actual offer depends on relevant skills, experience, education, training, and work location.

How Jobgether works

We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.

We appreciate your interest and wish you the best!

Why Apply Through Jobgether?

Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.

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