01 / Best Role Fit
Senior and staff-level systems, compiler, or AI infrastructure roles.
My strongest work happens when low-level correctness, product pressure, and operational reality all touch the same system.
Omar Habra
I build systems for teams that cannot afford shallow engineering: compiler work, performance-sensitive platforms, accelerator bring-up, and product-facing AI systems that still have to ship cleanly.
Shipped In
Apple, SiFive, Stealth
Compiler teams, performance engineering, and product-facing ML delivery.
Core Depth
LLVM, Swift, TTNN
Public proof across compiler work and accelerator bring-up.
Operating Range
AWS, Azure, GCP
Multi-cloud systems with automation, retrieval, and reliability guardrails.
Current Search
Senior / Staff
Roles where systems judgment and shipping discipline both matter.
Search Brief
Less portfolio decoration, more signal about where I fit, how I work, and what a team can verify quickly.
01 / Best Role Fit
My strongest work happens when low-level correctness, product pressure, and operational reality all touch the same system.
02 / Fastest Proof
Open the Swift compiler PR, the TT-Metal pull requests, or DiffSwarm and you get concrete artifacts instead of portfolio theater.
03 / How I Operate
I like difficult work, but I do not like drama. The point is to make the system clearer, safer, and easier to move.
Selected Proof
Four settings where the job changed, the constraints were real, and the work can still be inspected by a technical hiring panel.
Context
Mandate
Proof
Constraint
ABI mismatches and codegen regressions were expensive to get wrong.
Ownership
Bridged AST and ABI behavior, validated cross-architecture code generation, and built regression tooling for safer rollout.
Impact
Shipped interoperability work with stronger confidence across x86_64 and arm64 targets.
Operating Style
I keep this section because it says something useful about how I work: train deliberately, review the telemetry, and stay composed when the conditions get ugly.
Why it matters
Not hobby theater. More evidence about judgment, repetition, and how I handle long feedback loops.
Race Telemetry
Real route data from actual races
01
After-action review through route traces, splits, and footage.
02
Pace patiently, then push when the data says the system can hold.
03
Preparation, stress tolerance, and clean retros are part of the same muscle.
Training lens
Telemetry
Route files, splits, and footage over vibes.
Temperament
Composed
Long races and bad debugging sessions reward the same calm.
Transfer
Deliberate prep
Training blocks and release plans rhyme more than people think.

First marathon
Four months of structured training from zero to finish line.

Purple belt
Repeated adaptation under pressure, not just one good day.

Olympic distance
Swim, bike, and run with telemetry worth reviewing afterward.
Public Receipts
Good hiring conversations get faster when the proof is one click away.
Open Product
A multi-agent review CLI where one model proposes bugs, another challenges them, and the final report keeps only evidence-backed findings.
Visit the productCompiler Work
Public compiler contribution adding getter and setter interoperability to the official Swift toolchain.
Read PR #40842Accelerator Work
MaskFormer, DPT-Large, and YOLOS-small bring-ups for novel hardware, with implementation detail and artifacts in the open.
See TT-Metal PRsContact
The best outreach includes the role, the hard part of the system, and why you think my background maps to it.