Hire Remote Performance Engineers

Hire Performance Engineers Who Find Bottlenecks Before Your Users Do

A slow application costs revenue, trust, and rankings — simultaneously. A 1-second delay in page load reduces conversions by 7%. A single production outage under traffic spike can cost more than months of engineering work. Performance problems found in production are 100x more expensive to fix than performance problems found in development.

We match you with senior Performance Engineers who’ve profiled, load-tested, and optimized high-traffic applications for enterprise SaaS companies, e-commerce platforms, and consumer applications. Engineers who understand performance at every layer — application code, database queries, network latency, caching architecture, and infrastructure scaling — and who build the load testing infrastructure to validate improvements, not just identify problems.

Start in days, not months. Pay 50% less than equivalent US-based performance engineering talent.

What Our Performance Engineers Do

Load & Stress Testing

Designing and executing realistic load tests that simulate production traffic patterns — not just peak concurrency numbers, but realistic user journeys, think times, data distributions, and geographic distribution. k6, Gatling, JMeter, and Locust-based load testing integrated into CI/CD pipelines.

Application Performance Profiling

Code-level profiling with APM tools (New Relic, Datadog APM, Dynatrace, py-spy, async-profiler) to identify hotspots: slow functions, N+1 query patterns, inefficient algorithms, and serialization bottlenecks. Fixing the right 20% of code that causes 80% of latency.

Database Query Optimization

EXPLAIN ANALYZE for PostgreSQL and MySQL, index design for query patterns, N+1 query elimination in ORM-heavy applications, connection pooling tuning, and database caching strategies (Redis, Memcached, materialized views) that reduce latency by orders of magnitude.

Infrastructure Scaling Design

Horizontal and vertical scaling strategies, auto-scaling configuration, CDN optimization, load balancer tuning, and the infrastructure architecture decisions that allow systems to absorb 10x traffic spikes without degradation.

Core Web Vitals & Frontend Performance

Lighthouse, WebPageTest, and browser profiling to optimize LCP, INP, and CLS — the Google Core Web Vitals that affect both user experience and search rankings. Bundle optimization, image optimization, critical rendering path, and caching strategies for frontend performance.

Performance Engineering Stack

Load Testing: k6, Gatling, Apache JMeter, Locust, Artillery, BlazeMeter

APM: Datadog APM, New Relic, Dynatrace, Elastic APM, AWS X-Ray

Profiling: async-profiler (JVM), py-spy (Python), pprof (Go), Instruments (iOS), Xdebug (PHP)

Databases: PostgreSQL, MySQL, MongoDB, Redis, Elasticsearch — query optimization and caching

Frontend: Lighthouse, WebPageTest, Chrome DevTools, webpack-bundle-analyzer, Cloudflare

Infrastructure: AWS, GCP, Azure — auto-scaling, CDN, load balancers, connection pooling

Client Success Story: E-Commerce Platform — Survived Black Friday at 50x Normal Traffic

A mid-market e-commerce company had experienced two consecutive Black Friday outages — their systems collapsed under 10x normal traffic, costing an estimated $800K in lost sales each time. Our Performance Engineer conducted a 6-week performance engineering engagement before their third Black Friday: load tested their checkout flow with realistic traffic patterns, found 3 N+1 query chains in their product listing API (each API call generated 47 database queries), tuned their PostgreSQL connection pool (default config was causing connection starvation), and implemented Redis caching for their product catalog. The load test results supported a 60x traffic capacity. Black Friday processed 50x normal traffic with 99.97% availability. Revenue for that Black Friday: 3.4x the previous year.

Client Success Story: SaaS Platform — P99 Latency from 8.2s to 340ms

A B2B SaaS company’s core reporting API had a P99 latency of 8.2 seconds — reports that took 8 seconds to load were causing churn among power users. Our Performance Engineer profiled the API with Datadog APM, identified a reporting query that performed a full table scan on a 200M-row table, designed a composite index that reduced the query from 6.8 seconds to 180ms, implemented query result caching with a 5-minute TTL for reports with the same parameters, and refactored a background aggregation job to pre-compute the most common report filters. P99 latency dropped from 8.2 seconds to 340ms. Power user churn rate dropped 31% in the following quarter.

Why Companies Choose Our Performance Engineers

• Full-stack profiling: They find performance problems at the right layer — not just tune infrastructure when the problem is in application code
• Load test realism: They model realistic traffic patterns, not just concurrency counts — their tests find production problems
• Root-cause fixers: They don’t just identify slow queries — they fix them, validate the fix, and build regression prevention
• 50% cost savings: Senior performance engineering expertise at a fraction of US market rates
• Fast start: Most engagements begin within 1–2 weeks

Engagement Models

• Performance Engineering Sprint — A focused 4–8 week engagement: baseline, profiling, load testing, fix implementation, and re-validation. Targeted at specific performance goals.
• Embedded Performance Engineer — Ongoing embedded performance engineering, integrating load testing into CI/CD and owning performance as a continuous engineering concern.
• Pre-Launch Performance Assessment — Comprehensive performance assessment and load testing before a major launch or traffic event (product launches, Black Friday, marketing campaigns).
• Contract-to-Hire — Evaluate a performance engineer’s profiling and optimization approach before committing long-term.

How To Vet Performance Engineers

Our vetting identifies performance engineers who fix root causes — not just document performance problems.

1. Profiling case study — Walk us through a specific performance problem they diagnosed and fixed. What was the symptom, what was the root cause, how did they find it, and what was the before/after measurement? We probe for root cause specificity and measurement rigor.
2. Load test design challenge — Given a web application and a business performance requirement (e.g., “support 10,000 concurrent users with P95 latency under 500ms”), how do they design the load test? We assess traffic modeling sophistication, not just tool knowledge.
3. Database optimization exercise — Given a slow query and a schema, analyze the query and propose index and query optimization. Evaluated on EXPLAIN ANALYZE interpretation and index design reasoning.
4. Infrastructure scaling discussion — How do they determine whether a performance problem is an application problem vs. an infrastructure scaling problem? What instrumentation tells them the answer?

What to Look for When Hiring Performance Engineers

Strong performance engineers find and fix root causes — they don’t just run load tests and report numbers.

What strong candidates demonstrate:

• They have specific before/after latency numbers for performance improvements they’ve made — not vague “improved performance”
• They profile with APM tooling and code profilers — they don’t guess at bottlenecks or just add caching to everything
• They build load tests that model realistic traffic patterns — they understand think time, data distribution, and geographic latency simulation
• They’ve optimized at multiple layers: code, queries, caching, and infrastructure — not just one layer

Red flags to watch for:

• Performance optimization means “I added a Redis cache and a CDN” without profiling to identify what was actually slow
• Load tests are concurrency tests only — no realistic user journeys, no think time, no data variation
• Can’t read a database EXPLAIN ANALYZE output — can identify slow queries by execution time but can’t diagnose why they’re slow
• No APM or profiler experience — identifies performance problems by reading application logs rather than profiling

Interview questions that reveal real depth:

• “Walk me through a performance problem you solved that required fixing code rather than scaling infrastructure. What was the bottleneck and how did you find it?”
• “How would you design a realistic load test for a multi-step checkout flow? What user behaviors, data patterns, and think times would you model?”
• “A PostgreSQL query that was running in 50ms is now running in 8 seconds after the table grew from 1M to 50M rows. Walk me through your diagnostic approach.”

Related Services

• DevOps & SRE Engineers — Infrastructure engineers who build the scalable architecture that performance engineers validate.
• SDETs — Test infrastructure engineers who integrate performance testing into CI/CD pipelines.
• QA Managers — Quality engineering leaders who own performance testing as part of the overall quality strategy.
• Full-Stack Developers — Application engineers who implement the code-level fixes performance engineers identify.

Want to Hire Remote Performance Engineers?

We source, vet, and place senior Performance Engineers who find and fix the bottlenecks that cause slow applications and production outages — engineers who profile at the code level, design realistic load tests, and implement fixes that produce measurable latency and throughput improvements. Whether you need a focused performance sprint or ongoing performance engineering, we make it fast, affordable, and low-risk.

Get matched with Performance Engineers →

Ready to hire Performance Engineers who make your application fast under real production load? Contact us today and we’ll introduce you to senior performance engineers within 48 hours.